Just me..and I'm Back!

Evolution of Mobile & Wireless Networks

2025-10-24T20:58:00.000-07:00

UniMarconi-FinalPrint — HTML (A4 pages)

Via Plinio 44 - 00193 Roma  
Tel. +39 06 377251  
www.unimarconi.it 
info@unimarconi.it  
Codice Fiscale e Partita IVA: 07154361005    
 
 
 
 
 
UNIVERSITA’ DEGLI STUDI GUGLIELMO MARCONI 
Master of Science in Data Science & Artificial Intelligence (AI) 
 
 
 
 
Mobile& Wireless Networks – Evolution from 1980s to 5G and Beyond.  
 
 
Academic Advisor Candidate 
DHANABAL T         AZRIN ABDUL MAJID 
Lithan Academy        Rapheal Azrin 
MCSLT00084

Page 1

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 2 of 114  UNIVERSITA’ DEGLI STUDI GUGLIELMO MARCONI 
ACADEMIC YEAR 2025  
Table of Contents  
Section Page 
Chapter 1 Introduction & Research Methodology 1 
 Research & Discussion on Possible Topics 5 
 Brainstorming 7 
Chapter 2 - Literature Review 9 
 Thesis Preface & Introduction  11 
 Candidate Background 12 
Chapter 3 - Thesis Abstract 13 
 Thesis Statement 14 
 Dedication and Thanks 15 
Chapter 4 - Historical Timeline (1G to 5G and early 6G research)  22 
Chapter 5 - Spectrum & Regulation (ASEAN/ANZ focus, 900 MHz, mmWave, etc.)  32 
 Annex - AI & ML for Propagator Prediction  38 
Chapter 6 - Modulation, Coding, Multiple Access  39 
Chapter 7 - Network Topologies & Hierarchies  44 
Chapter 8 - Standards by Generation (1G–5G, WiFi, WiMAX, LoRaWAN)  51 
Chapter 9- Specialized Systems & Use Cases  
(LoRaWAN, TETRA ATEX, ST GRID, Satellites: Beidou, Starlink, Inmarsat)  58 
Chapter 10 - Core Networks (MSC to 5GC with AI/ML enhancements)  65 
Chapter 11 - Security & Authentication  71 
Chapter 12 - Performance, QoS, KPI Engineering (with Data Science)  76 
Chapter 13 - Deployment, Optimization, Testing (AI-driven SON, ML analytics)  81 
Chapter 14 - Comparative Case Studies (ASEAN & ANZ)  86 
Chapter 15 - 5G Advanced & Road to 6G  91 
Chapter 16 - Conclusion  96 
Chapter 17 - References & Bibliography  101

Page 2

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 3 of 114   
Chapter 1 Introduction  
 
This paper is to demonstrate the competence level of the Postgraduate Masters Degree 
by Coursework  candidate, Mr Azrin Bin Abdul Majid  aka RAPHEAL AZRIN , to 
the competency level for the course on Data Science and Artificial Intelligence, in the 
academic year of 2025. 
 
The task assigned by the teaching staff is to gather information, determine a sub-
deviation of the topics being tabled, and carefully expand and research into the subject 
matters, in a miniature brief of 12,000 worded paper , with graphics et al.  
 
A Long list of topics and their Research Methodology were proposed. 
 
Added to the ask is that, the usage of AI ,DATASCIENCE, Cybersecurity et al, as the 
candidate is attempting a Masters of Science in Data Science and AI  under Lithan-
UniMarconi collaboration and executive Masters program as part of the Skill future 
Career Transition Program, with 90% Funded by the Singapore Government vis 
Skillfuture Credits + CoPayment by the student on the PGDip segment. 
 
The Objective of the program is to ensure the student can demonstrate the ability to 
apply and use case of the knowledge and skills learnt thru the SCTP Module , Data 
Science Modelling, Deep Learning, AML & Methods, Data Science Principles, Python 
for DS, DMV Data Modelling & Visualization, Generative AI & Project Management. 
  
Deliberations of the modelling and reasoning brings us to the below mentioned. 
Following are the proposed topics tabled by the senate as appropriate for the cohort for 
Data Science, Security Management and Cloud Administration. 
 
Generative and Agentic AI Agent services were used in the research to expedite results 
and research, cross referencing against the generative bias of agents such as 
ChatGPT5 , CoPilot365 , Gemini Enterprise  and DeepSeek  alongside other agentic

Page 3

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 4 of 114  workflow systems in place. CoPilot was also activated in Office365 to assist in writing 
and editing of the paper. All AI Systems have TRUTH MODE manually coded into the 
prompts, so no false information were produced and fact checked on all counts. 
 
However, the candidate believes these three cohorts could be integrated into the overall 
master's program, allowing their skillsets to intersect and complement each other.

Page 4

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 5 of 114   
Research and Discussion on Possible Thesis Topics  
 
Looking back at the subject matter, these are the best options proposed by the cohort 
and teaching staff for attempt, based on current capabilities, educational background 
and course outline: 
 AI-Driven Cybersecurity in Mobile & Wireless Systems  
How AI/ML enhances anomaly detection, intrusion prevention, and resilience 
in wireless networks. 
 Data Science for Distributed Databases and Secure Data Management  
Using predictive analytics, ML, and automated database management for 
secure, large-scale distributed data systems. 
 Intelligent Error-Correcting Codes and Cryptography  
Application of AI to optimize coding algorithms (e.g., convolutional codes, 
Reed-Solomon) and cryptographic schemes for secure communication. 
 
Brainstorming 3 possible topics  
 
After much deliberation, the team members further refined and suggested the following 
topics for attempt: 
1. AI-Powered Intrusion Detection Systems for Next-Generation Wireless 
Networks  
2. Data Science Approaches to Secure and Efficient Distributed Database 
Management  
3. Machine Learning Optimization of Error-Correcting Codes and 
Cryptographic Algorithms for Cybersecurity

Page 5

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 6 of 114  Thesis Statements on the 3 possible topics  
 
As candidate will be attempting a PhD Styled thesis, these thesis statements would 
be used to propose to the senate for the purpose of the attempted subjects. 
Draft Statements for MS-PhD level Abstract and synopsis : 
 
1. AI-Powered Intrusion Detection Systems for Next-Generation Wireless 
Networks  
This thesis investigates the integration of machine learning models into 
intrusion detection systems (IDS) for 5G/6G wireless infrastructures, aiming to 
enhance anomaly detection accuracy and reduce false positives, thereby 
strengthening cybersecurity in mobile environments.  
2. Data Science Approaches to Secure and Efficient Distributed Database 
Management  
This research explores how advanced data science techniques, including 
predictive analytics and automated anomaly detection, can optimize 
performance, reliability, and security in distributed database systems handling 
sensitive and large-scale data.  
3. Machine Learning Optimization of Error-Correcting Codes and 
Cryptographic Algorithms for Cybersecurity  
This thesis develops AI-enhanced optimization models for coding algorithms 
and cryptographic schemes, with the goal of improving error resilience, 
throughput, and resistance against cyber threats in high-speed network 
communications.  
 
 
 
 
END OF CHAPTER 1

Page 6

Each of the proposed topics were presented with a set of Literature Review and
Directional input as to the how the research would be laid out.

Topic 1: AI-Powered Intrusion Detection in Wireless Networks
 Foundations : Intrusion Detection Systems (IDS), anomaly vs. signature-based
detection.
 Current Research : ML in IDS (e.g., Random Forest, CNN, RNN for network
traffic).
 Gap: High false positive rates, adaptability in mobile/wireless contexts.
 Key Authors/Journals : IEEE Communications Surveys, ACM Computing
Surveys, Elsevier Computers & Security.
Topic 2: Data Science in Distributed Databases
 Foundations : CAP theorem, distributed DB management (MongoDB,
Cassandra, etc.).
 Current Research : AI for query optimization, automated fault detection.
 Gap: Balancing performance, security, and reliability in real-time.
 Key Authors/Journals : VLDB Journal, IEEE Transactions on Knowledge and
Data Engineering.
Topic 3: AI-Optimized Error-Correcting Codes & Cryptography
 Foundations : Cyclic codes, convolutional codes, Reed-Solomon, block
ciphers.
 Current Research : AI for decoding optimization, cryptographic key prediction
prevention.
 Gap: Integrating ML with coding/crypto without introducing vulnerabilities.
 Key Authors/Journals : IEEE Transactions on Information Theory, Journal of
Cryptology.

Page 7

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 8 of 114   
 
However, the preliminary subject that I will be attempting will be mobile and wireless 
networks system  as a primary subject.  
Advisor also recommended to keep the wording to 12,000 words in context at the MSc 
level, and expand should I dedicate to have this paper be expanded further at the PhD 
level later on in the year. 
The take on TOPIC 1  is good, but given the density of the research, it would be good 
to do it at the PhD Level, once this paper has been published on Google Scholar.

Page 8

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 9 of 114  Background of candidate  
 
About Rapheal Azrin 
 
My Profile as a Regional IM/IT Digitalization & Technology Manager  at Shell 
Petroleum  provides me a vast knowledge of know-how in the field of 
telecommunications and I felt I should showcase my git into this paper.  
 
Candidate is also an ICANN IANA Working Stakeholder member , collaborating for 
RFC and IEEE technical papers.  
 
Candidate also has more than 30 years of experience in the relevant field, specializing 
in Web Hosting, Telecommunications and Intellectual Properties.  
 
Something noteworthy is that the candidate an IEEE Chartered Fellow in the field of 
WDRP Domain Names and Dispute Arbitration, in the aspect of copyright & 
Intellectual Property infringement. 
 
Public information can be gathered across search engines, Crunchbase and Linkedin. 
 
https://www.linkedin.com/in/azrinmajid  - Linkedin Profile 
 
https://icannwiki.org/Raphael_Azrin  - ICANN WIKI 
 
https://www.crunchbase.com/person/rapheal-azrin  - Crunchbase 
 
He is also an advocate for crypto & digital assets in using blockchain as an encoding 
technology such as Blake17,Blake19 shuffle et al. 
 
 
 
END OF CHAPTER 2

Page 9

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 10 of 114  Chapter 3 - Abstract  
 
This thesis presents a comprehensive, engineering-focused analysis of mobile and 
wireless radio systems, charting their evolution from analog cellular networks of the 
1980s through the era of 5G New Radio and into the initial phases of 6G research. 
Emphasis is placed on the technical lineage of air interfaces—progressing from FDMA 
and analog FM to TDMA, CDMA, OFDMA, and massive MIMO—and situates these 
innovations within the broader contexts of spectrum allocation, propagation 
fundamentals, and regulatory policy frameworks across ASEAN, APAC, Europe, and 
ANZ. 
 
Topological perspectives are integrated, encompassing macro, micro, PICO, FEMTO, 
LPWAN, ad hoc, and satellite constellation architectures. The study details the 
progression of core network technologies, from circuit-switched Mobile Switching 
Centers (MSCs) to 5G Service-Based Architectures (SBA) and the advent of AI-native 
cores. Security and authentication mechanisms receive special focus, tracing the shift 
from legacy analog protocols to modern SIM-based authentication, mutual AKA, eSIM 
provisioning, SUCI identifier protection, and quantum-safe, forward-looking 
approaches. 
 
Comparative case studies of deployments in Singapore, Malaysia, Japan, and Europe 
illuminate how local geography, regulation, and socio-economic conditions have 
shaped divergent paths of technology adoption. Special systems—including TETRA 
for mission-critical voice, LoRaWAN for IoT, satellite backhaul (LEO/MEO/GEO), 
WiMAX, Wi-Fi, Bluetooth, ZigBee, and UWB—are examined for their technical and 
operational trade-offs. An addendum develops the Smart Nation case study, 
highlighting Singapore’s PSA Tuas Mega Port, autonomous vehicles, and urban IoT 
ecosystems as precursors for advanced research into smart infrastructures. 
 
The thesis also addresses the integration of Artificial Intelligence and Data Science,

Page 10

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 11 of 114  covering predictive resource allocation, anomaly detection, self-optimizing networks 
(SON), and machine learning-driven security as key enablers for managing next-
generation system complexity. Ultimately, the work argues that the progression of 
mobile and wireless systems is not merely a pursuit of speed, but a convergence of 
communication, computation, and control, underpinning the Fourth Industrial 
Revolution. Concluding with a forward-looking perspective, the thesis discusses 
developments in 5G-Advanced and 6G research, including sub-THz spectrum, 
reconfigurable intelligent surfaces (RIS), integrated sensing and communication 
(ISAC), and post-quantum security, positioning this work as both a technical record and 
a strategic roadmap for future scholarly and industrial exploration.

Page 11

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 12 of 114  Thesis Statement  
 
This thesis critically examines the technical evolution, socio-economic implications, 
and prospective development of mobile and wireless radio systems, tracing their 
advancement from first-generation (1G) analog voice networks to contemporary 5G AI-
native infrastructures and emerging 6G paradigms. It posits that mobile networks have 
transcended their original role as communication platforms, becoming essential digital 
infrastructure that underpins smart nations, industrial automation, and socio-economic 
growth. By integrating engineering analysis, comparative regional case studies across 
ASEAN, APAC, Europe, and ANZ, and perspectives from Artificial Intelligence and 
Data Science, this research demonstrates that the design and operation of wireless 
systems represent a convergence of spectrum policy, architectural innovation, and 
intelligent automation. The thesis contends that the future of wireless connectivity will 
be defined by AI-enhanced, secure, and context-aware architectures, capable of 
supporting both mass-market services and mission-critical applications, thereby 
establishing 5G-Advanced and 6G as foundational enablers of the Fourth Industrial 
Revolution.

Page 12

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 13 of 114  Dedication and Thanks  
 
I am dedicating this thesis to my beloved daughter, IZZATI MARDIAH AZRIN  
whom passed away on 10th September 2025 while I am writing this paper. Although it 
was a crushing blow to my emotional state of mind and to our family during this darkest 
moments, I strive to carry on to complete this paper , in whatever way possible for the 
better of others. From Allah we come, To Him we return. Nothing happens without the 
will of God. 
 
Candidate is appreciative for the support during these times to the following persons 
 
JeyaShree Rajkumar  – Senior Program Manager, Lithan Academy for the inputs on 
the usage of Artificial Intelligence, Prompting, Programming, Workflow designs 
which have been heavily utilized in this program and the presentation of the thesis. 
 
Andrew Toh Tze Chow  – Adjuct Faculty Lecturer - Artificial Intelligence 
Regression and Progression Modelling techniques 
https://www.linkedin.com/in/andrew-toh78/  
 
Fan Yeng Loon  – Adjuct Faculty Lecturer – Machine Learning Technical techniques 
crucial insights to the technological Data Science Analytics 
 
Dhanabal T  – Lithan Academy / UniMarconi Adjuct Lecturer – Basis of Encyrptions 
in Data Communications 
 
Dr Azian Razak  – Zyraz Technology – CXO – ZYRAZ Global Technologies – 
Scaleup systems in industrialised environments optimized using Artificial Intelligence 
, Machine Learning and Data Science Analytics. Linkedin : 
https://www.linkedin.com/in/azianrazak  
 
 
Dr M Rezuwan Zakaria  – Sentara group – Neste Oils – Usage of Machine Learning 
Modelling in real time, JIT systems from Research to full scale. 
https://www.linkedin.com/in/rezuwanzakaria  
 
Also many thanks to fellow cohort mates of PDDS0524 of the Lithan Academy:  
Mahathir Humaidi - Collaboration classmate & brainstorming of the subject matters 
– Program Manager, Lithan Academy 
https://www.linkedin.com/in/mahathir-humaid

Page 13

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 14 of 114   
Aloysius Chia -  Collaboration Classmate,AML/KYC Specialist – HSBC Singapore 
https://linkedin.com/in/aloysiuschia  
 
Justin Neo  - Collaboration Classmate – Business Development – HSBC Singapore 
https://www.linkedin.com/in/justin-neo-01140047  
 
Jimmy Seah -  Collaboration Classmate – Director – BECA 
https://www.linkedin.com/in/jimmyseah/  
 
Ng Yea Ling  – Collaboration Classmate – PICO Creative 
https://www.linkedin.com/in/yea-ling-ng-578779171/

Page 14

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 15 of 114  Research & Scope  
 
 
The evolution of mobile and wireless communication systems over the last four decades 
represents one of the most transformative engineering and societal achievements in 
human history. From the humble beginnings of analog cellular telephony in the 1980s 
(1G) to today’s highly sophisticated 5G networks and the research frontier of 6G, 
mobile and wireless technologies have fundamentally reshaped the global economy, 
connectivity paradigms, and the nature of human interaction. This thesis presents a 
comprehensive engineering-oriented overview of mobile and wireless radio 
systems, emphasizing their technical evolution, underlying architectures, and 
deployment topologies , while integrating perspectives from data science, artificial 
intelligence (AI), and machine learning (ML).  
 
The study addresses both historical context  and forward-looking perspectives , 
incorporating systems such as Wi-Fi, WiMAX, LoRaWAN, TETRA ATEX radios, ST 
GRID devices, and non-terrestrial networks including satellite constellations such as 
Beidou, Gouwang, Qianfan, Starlink, and Inmarsat. It also provides a comparative 
regional analysis focusing on ASEAN (Association of Southeast Asian Nations)  and 
ANZ (Australia and New Zealand)  markets, where diverse geographic, demographic, 
and regulatory conditions have shaped unique deployment trajectories.  
 
The relevance of this topic is manifold. From an engineering standpoint , the radio 
technologies that underpin mobile networks illustrate the interplay between spectrum 
allocation, modulation techniques, hardware capabilities, and topological design.  
From a data science and AI perspective , modern networks have become too complex 
to manage solely through human intervention; they rely increasingly on autonomous 
optimization, predictive analytics, and AI-native architectures . From a socio-
economic angle , mobile and wireless networks have become essential infrastructures—
particularly evident during the COVID-19 pandemic when connectivity underpinned 
remote work, education, and healthcare.

Page 15

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 16 of 114  Thus, this thesis aims to:  
1. Trace the technical lineage  of mobile and wireless systems from 1G to 5G and 
beyond, including parallel technologies such as Wi-Fi, WiMAX, and LPWAN.  
2. Analyze the role of spectrum and regulation , with a specific emphasis on the 
historically significant 900 MHz band and spectrum management policies in 
ASEAN and ANZ.  
3. Examine propagation fundamentals  and how link budgets drive engineering 
design. 
4. Investigate modulation, coding, and multiple access schemes  as the building 
blocks of radio systems.  
5. Compare network topologies and hierarchies , from macro-cells to 
femtocells, as well as mesh and ad hoc structures.  
6. Explore specialized systems  including mission-critical communications 
(TETRA ATEX, ST GRID) and satellite networks.  
7. Assess the role of AI, ML, and Data Science  in optimization, performance 
analysis, and next-generation innovations.  
8. Provide regional case studies  (ASEAN and ANZ), highlighting opportunities 
and challenges.  
9. Conclude with a forward-looking view  on 5G Advanced, 6G research themes, 
and the growing AI-native network paradigm.

Page 16

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 17 of 114  Research Methodology  
 
The methodology employed in this thesis adheres to a mixed qualitative–quantitative 
framework , designed to ensure both academic rigor  and practical engineering 
applicability . 
 
Literature Review and Bibliographic Analysis  
 Primary Sources : 
o 3GPP specifications (TS 36.xxx for LTE, TS 38.xxx for 5G NR).  
o ITU reports (IMT-2000, IMT-Advanced, IMT-2020).  
o IEEE standards (802.11 Wi-Fi, 802.16 WiMAX).  
o LoRa Alliance technical documentation.  
 Secondary Sources : 
o Peer-reviewed academic journals (IEEE Transactions on Wireless 
Communications, Elsevier Computer Networks, ACM SIGCOMM).  
o Industry white papers (Ericsson, Nokia, Huawei, Qualcomm, Cisco).  
o Regulatory reports from ASEAN/ANZ agencies (e.g., ACMA in 
Australia, IMDA in Singapore, NTC in the Philippines).  
 Grey Literature : 
o Satellite operator documentation (Inmarsat technical manuals, Starlink 
deployment updates, Chinese satellite navigation whitepapers for 
Beidou/Gouwang/Qianfan).  
o Operator field reports (Singtel, Telstra, Globe Telecom).  
The literature review ensures triangulation of perspectives: academic theory, industry 
practice, and regulatory frameworks .

Page 17

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 18 of 114  Comparative Case Study Method  
 
The thesis uses comparative case studies  for ASEAN and ANZ to illustrate how 
geographic, economic, and regulatory differences influence mobile system deployment.  
 ASEAN: diverse spectrum of markets, from advanced (Singapore) to 
developing (Cambodia, Myanmar). Key themes: urban-rural divide, 
affordability, spectrum scarcity.  
 ANZ: large land masses, low population density, leading to emphasis on 
satellite integration and rural coverage.  
Case studies will be supported by quantitative data (coverage, latency, throughput, 
spectrum allocation)  and qualitative analysis (policy, regulation, operator 
strategies).  
 
 
Engineering Analysis 
 
The thesis employs engineering evaluation methods , including:  
 Link budget modeling : examining transmit power, antenna gains, path loss 
models (COST-Hata, ITU-R).  
 Spectral efficiency comparisons  across generations (bits/s/Hz).  
 Latency and throughput trade-offs  in different access schemes (TDMA vs 
CDMA vs OFDMA).  
 QoS/KPI assessment  with AI-based predictive modeling (supervised ML for 
anomaly detection, clustering for traffic profiling).

Page 18

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 19 of 114  AI and Data Science Integration  
 
Modern networks rely heavily on AI/ML techniques. This thesis incorporates:  
 AI-native RAN (Radio Access Networks):  Machine learning for beam 
management, anomaly detection, and interference prediction.  
 Data Science for KPI Optimization:  Predictive analytics for handover success 
rates, coverage holes, and traffic distribution.  
 Big Data in Core Networks:  Cloud-native 5G cores leveraging ML-based 
orchestration.  
 AI-enhanced Security:  ML-based intrusion detection and adaptive encryption.

Page 19

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 20 of 114  Validation 
 
To ensure validity and reliability:  
 Cross-verification  of technical details with multiple sources (academic + 
industry + regulatory).  
 Quantitative benchmarks  aligned with ITU/3GPP standards.  
 Transparency in assumptions  (e.g., propagation models assume urban macro-
cell unless stated otherwise).  
 Clear citation and footnoting , following UniMarconi’s guidelines.  
 
Limitations 
 
 Rapid pace of 5G/6G research may render certain projections outdated.  
 Some proprietary satellite system details are not publicly disclosed.  
 Case studies rely on available regulatory/industry data, which may not be fully 
standardized across regions.  
 Thesis research is primarily focused on the APAC-ASEAN, specifically 
Singapore, Malaysia, Japan, ANZ clustering with some distinctive comparative 
legislations against the Euro EEC and Americas  
 Machine Learning bias may be implied in the research as agentic and deep 
research systems were used to derive the deep dive data.  
END OF CHAPTER 3

Page 20

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 21 of 114  Chapter 4 - Historical timeline – 1G to 5G and Beyond  
 
Introduction to the generational Evolution 
 
The Evolution of Mobile and Wireless communication networks were mainly defined 
by the CCITT or Consultative Committee for International Telegraphs and Telephone, 
based out of France and later on merged into the ITU or International 
Telecommunications Union ( www.itu.int ) which is now based out of Geneva, Swiss.  
The earliest way of communicating was using MORSE CODE through low band 
frequencies across the Atlantic thanks to WW2 where early German Enigma machine 
cause heavy casualties to the allied forces. 
 
The evolution of mobile communication networks from the 1980s to the present has 
been marked by a sequence of “generational leaps” defined by standardization bodies 
such as the International Telecommunication Union (ITU)  and the 3rd Generation 
Partnership Project (3GPP) . Each generation (1G, 2G, 3G, 4G, and 5G) introduced 
new radio access technologies, modulation schemes, network architectures, and 
service paradigms , with 6G already emerging as a research frontier. These 
generational shifts were not merely technological; they were shaped by regulation, 
spectrum availability, socio-economic factors, and hardware capabilities . 
The following sections trace this timeline in detail, highlighting technical 
characteristics, socio-economic impacts, and the role of AI and data science in shaping 
or analyzing each phase.

Page 21

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 22 of 114  4.1 First Generation (1G): The Analog Era (1981–1991)  
 
 
4.1.1 Overview  
1G systems marked the beginning of cellular telephony , replacing 
limited-range radio phones with large-scale mobile systems. They were 
fully analog , primarily designed for voice services , and lacked 
encryption or advanced mobility features.  
 
4.1.2 Key Standards  
AMPS (Advanced Mobile Phone System)  – deployed in North 
America, operating in the 800 MHz band with 30 kHz FM channels.  
NMT (Nordic Mobile Telephony)  – Scandinavia, 450/900 MHz bands.  
TACS (Total Access Communication System)  – UK, Italy, other 
European countries.  
4.1.3 Technical Features  
Multiple Access:  FDMA (Frequency Division Multiple Access).  
Bandwidth Efficiency:  <0.5 bits/s/Hz.  
Handover:  Hard handoff, circuit-switched connections.  
Security:  Minimal; cloning and interception common.

Page 22

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 23 of 114  4.1.4 Limitations  
 Very low capacity relative to spectrum usage.  
 No data services, SMS, or encryption.  
 Expensive handsets with limited battery life.

Page 23

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 24 of 114  4.2 Second Generation (2G++): Digital Cellular (1991–2001)  
 
   
 
4.2.1 Overview  
2G introduced digital modulation , which enabled greater spectral 
efficiency, voice encryption, and SMS services . It was the first 
generation where mobile phones became mass-market consumer 
devices. 
 
4.2.2 Key Standards  
GSM (Global System for Mobile Communications)  – TDMA-based, 
widely deployed in Europe, Asia, Africa. Operated primarily in 
900/1800 MHz bands.  
IS-95 (cdmaOne)  – First commercial CDMA system, pioneered by 
Qualcomm, operating in the US.  
IS-136 (D-AMPS)  – North American digital TDMA system, less 
successful than GSM or CDMA.  
4.2.3 Technical Features  
Multiple Access:  TDMA (GSM), CDMA (IS-95).  
Encryption:  A5/1 cipher in GSM, improved security vs. 1G.  
Data Services:  GPRS (General Packet Radio Service) → “2.5G”, 
EDGE (Enhanced Data for GSM Evolution) → “2.75G”.  
Peak Speeds:  50–200 kbps.

Page 24

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 25 of 114  4.2.4 Socio-Economic Impact 
 
SMS revolutionized communication ; low-cost and widely adopted.  
Mobile penetration grew rapidly in ASEAN (e.g., Philippines’ “texting 
capital” phenomenon).  
In ANZ, 2G enabled widespread rural voice coverage.  
4.2.5 GPRS & EDGE 
 
Typically known as 2.5G and 2.75G, both Global Packet Radio Service 
and Enhanced Data for GSM Evolution plays a pivotal role in the next 
step of evolution into 3G networks. It also enables Laptops (aka Mobile 
Computers) to be really mobile so that it can be connected to the internet. 
Usage includes wireless Credit Card EFTPOS devices and Taxi Booking 
MDT. (shown: MDT POS system for Comfort Taxi in Singapore, circa 2000s)

Page 25

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 26 of 114   
4.3 Third Generation (3G): Multimedia & Internet (2001–2010)  
 
4.3.1 Overview  
3G was standardized under ITU’s IMT-2000 umbrella , promising 
global roaming and broadband-like services.  
 
4.3.2 Key Standards  
UMTS/W-CDMA  – Europe and Asia, 5 MHz carriers.  
CDMA2000 (1xRTT, EV-DO)  – North America and parts of Asia-
Pacific. 
4.3.3 Technical Features  
Multiple Access:  Wideband CDMA (UMTS), CDMA2000.  
Peak Speeds:  
o UMTS ~384 kbps,  
o HSPA (High Speed Packet Access) up to 14 Mbps (later 
HSPA+).  
Core Network:  Introduction of packet-switched cores alongside circuit-
switched.  
4.3.4 Limitations  
High cost of licenses in some countries (e.g., Europe).  
Spectrum fragmentation slowed adoption in ASEAN.  
ANZ markets adopted 3G early (Telstra’s Next G network).  
Different Networks not cross compatible between US and Rest of 
World.

Page 26

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 27 of 114  4.4 Fourth Generation (4G): The IP Broadband Era (2009–2019)  
 
4.4.1 Overview  
4G represented a paradigm shift: all-IP networks, high-speed 
broadband, and LTE as the global standard . 
 
4.4.2 Key Standards  
LTE (Long Term Evolution)  – OFDMA downlink, SC-FDMA uplink.  
WiMAX (802.16e/m)  – Considered 4G by ITU, but commercially 
outcompeted by LTE.  
4.4.3 Technical Features  
Multiple Access:  OFDMA (downlink), SC-FDMA (uplink).  
Bandwidth:  Up to 20 MHz per carrier; carrier aggregation up to 100 MHz.  
Peak Speeds:  100 Mbps (Release 8 LTE), later >1 Gbps with LTE-Advanced.  
Core Network:  Evolved Packet Core (EPC), all-IP, IMS/VoLTE for voice.  
4.4.4 Impact  
Enabled mobile broadband, video streaming, app economy.  
ASEAN: accelerated smartphone adoption, mobile financial inclusion.  
ANZ: Telstra and Optus pioneered early LTE rollouts for rural 
broadband.

Page 27

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 28 of 114  4.5 Fifth Generation (5G): Ultra-Flexible Architecture (2019–Present)  
 
4.5.1 Overview  
5G is not just a faster network but a flexible, software-defined 
platform  supporting enhanced mobile broadband (eMBB), ultra-
reliable low-latency communication (URLLC), and massive 
machine-type communication (mMTC).  
 
4.5.2 Key Standards  
3GPP Release 15 (2019)  – 5G NR, NSA/SA modes.  
Release 16–17  – URLLC, industrial IoT, non-terrestrial networks (NTN).  
Release 18 (5G Advanced)  – AI-native RAN, enhanced XR, positioning. 
 
4.5.3 Technical Features  
Multiple Access:  Flexible OFDM numerologies (15–240 kHz spacing).  
Spectrum:  
o FR1 (sub-6 GHz, e.g., 3.5 GHz).  
o FR2 (mmWave, 24–40+ GHz).  
Peak Speeds:  10 Gbps in lab trials.  
Latency:  <1 ms target for URLLC.  
MIMO: Massive MIMO (64T64R, >256 antennas), beamforming.  
4.5.4 Use Cases  
Smart cities, autonomous vehicles, industrial automation.  
ASEAN: Singapore leads with 5G SA, others catching up.  
ANZ: Focus on industrial IoT and rural coverage with low-band 5G.

Page 28

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 29 of 114  4.6  Towards Sixth Generation (6G): The Research Frontier (2030+) 
 
4.6.1 Emerging Concepts  
Sub-THz spectrum (100 GHz–1 THz)  for Tbps data rates.( Terabits)  
Joint Communication and Sensing (JCAS) . 
Reconfigurable Intelligent Surfaces (RIS).  
Integrated terrestrial–non-terrestrial networks (NTN).  
AI-native core and RAN.  
4.6.2 Global Research Initiatives  
Europe: Hexa-X program.  
China: 6G test satellite launches.  
Japan/Korea:  Government-industry consortia for 6G by 2030.  
ASEAN/ANZ:  Early research but more focused on 5G rollout.

Page 29

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 30 of 114   
4.7 Summary  
From analog voice (1G) to AI-native, cloud-driven architectures (5G and beyond), the 
mobile communications timeline reflects both technical ingenuity and socio-
economic drivers.  The integration of AI and Data Science  is not a late addition but a 
necessary response to the complexity of modern networks, paving the way for 6G as a 
cognitive, sensing-aware infrastructure.  
 
END OF CHAPTER 4

Page 30

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 31 of 114   
Chapter 5 - Spectrum and Regulation (ASEAN/APAC Focus, 900 
MHz and Beyond)  
 
5.1 Introduction to Spectrum as a Resource  
 
Spectrum is the most fundamental and limited natural resource in wireless 
communications. It is not consumable like fuel, but it is scarce and subject to strict 
allocation, regulation, and reuse strategies.  Unlike other resources, spectrum cannot 
be manufactured; it must be efficiently shared and reused using frequency planning, 
modulation efficiency, and multi-access schemes.  
The management of spectrum has been overseen globally by the International 
Telecommunication Union (ITU) , while national regulators such as ACMA 
(Australian Communications and Media Authority) , IMDA (Singapore) , NBTC 
(Thailand) , and others in the ASEAN region enforce localized frameworks. Spectrum 
licensing, auctions, and refarming are key mechanisms through which governments 
allocate frequencies to operators.  
 
5.2 The Importance of the 900 MHz Band  
 
The 900 MHz band has historically played a central role in mobile communications:  
 1G: Many early analog systems operated at 900 MHz.  
 2G GSM:  900 MHz was the global anchor band, offering balance between 
coverage and capacity.  
 3G UMTS:  Some refarming allowed deployment of 3G in 900 MHz.  
 4G LTE:  Operators in ASEAN/ANZ reused 900 MHz for LTE coverage in 
rural areas.  
 5G NR: While primarily sub-6 GHz and mmWave, some early deployments 
use refarmed 900 MHz for coverage-layer NR.

Page 31

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 32 of 114  5.3 Propagation Benefits  
 Lower path loss vs. higher bands.  
 Deeper indoor penetration.  
 Larger cell radius → fewer base stations needed, lowering CAPEX.  
5.4 Trade-offs  
 Narrower bandwidth compared to higher frequencies (limiting capacity).  
 Heavily congested in many regions due to legacy users.

Page 32

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 33 of 114   
 
5.5 Spectrum Allocation by Generation 
 
Generation  Typical Bands  Bandwidth Per Carrier  Notes 
1G 800–900 MHz  25–30 kHz  Analog FDMA channels  
2G (GSM)  900, 1800, 1900 MHz  200 kHz 900 MHz dominant in 
ASEAN/ANZ  
3G (UMTS/  
W-CDMA)  2.1 GHz (primary), 900 
MHz (refarmed)  5 MHz Used dual bands  
4G (LTE)  700, 800, 900, 1800, 2600, 
3500 MHz  1.4–20 MHz (CA up to 
100 MHz)  ASEAN/ANZ diversified  
5G (NR)  FR1 (600– 4200 MHz), FR2 
(24–40 GHz)  5–100 MHz (FR1), 100–
400 MHz (FR2)  Auctions across 
ANZ/ASEAN ongoing  
6G (Future)  Sub-THz (100–1000 GHz)  2–10 GHz  Research phase only

Page 33

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 34 of 114   
5.6 Spectrum Licensing Approaches  
5.6.1 Auction-Based Allocation  
 Common in ANZ (e.g., Australian 3.6 GHz 5G auction, 2018).  
 Generates government revenue but may increase operator costs → higher end-
user tariffs.  
5.6.2 Administrative Assignment  
 Seen in some ASEAN countries with less mature regulatory markets.  
 Spectrum directly assigned to incumbents.  
5.6.3 Unlicensed Spectrum  
 ISM bands (2.4 GHz, 5 GHz, 6 GHz) widely used for Wi-Fi and LoRaWAN.  
 ASEAN has mixed adoption of 6 GHz Wi-Fi 6E.  
 ANZ allows wider unlicensed use due to advanced regulatory frameworks.  
5.6.4 Shared Spectrum Models  
 CBRS (Citizens Broadband Radio Service) -style models in the US inspire 
ASEAN/ANZ regulators.  
 Potential use of shared spectrum for private 5G/IoT deployments.

Page 34

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 35 of 114  5.7 Spectrum Refarming  
Refarming is essential to migrate legacy networks without wasting spectrum:  
 ASEAN Example:  Philippines’ Globe Telecom reallocated 2G/3G bands to 
LTE and now to 5G.  
 ANZ Example:  Telstra and Optus shut down 2G by 2016–2017, refarming 
spectrum to LTE.  
Refarming balances backward compatibility with forward innovation.  
 
5.7.1 ASEAN Spectrum Landscape 
ASEAN nations are diverse in spectrum usage:  
Singapore (IMDA):  Aggressive 5G rollout in 3.5 GHz and mmWave. 
900 MHz used for LTE fallback.  
Malaysia (MCMC):  Single Wholesale Network for 5G (controversial).  
Thailand (NBTC):  Multi-band 5G auctions (700 MHz, 2600 MHz, 26 
GHz). 
Indonesia:  Large geography, limited 3.5 GHz spectrum availability → 
reliance on 1800/2100 MHz refarming.  
Philippines:  New entrant Dito Telecommunity licensed in 700 MHz for 
coverage.  
Challenges:  
Fragmented policies across countries.  
High auction fees vs. affordability pressures.  
Legacy 2G networks still operational in some regions.

Page 35

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 36 of 114  5.7.2 ANZ Spectrum Landscape  
Australia and New Zealand benefit from more coordinated spectrum 
planning . 
Australia (ACMA):  
o 700 MHz (“digital dividend”) allocated for LTE and 5G.  
o 3.6 GHz allocated to 5G.  
o mmWave auctions (26 GHz) completed in 2021.  
New Zealand (MBIE):  
o 600 MHz, 700 MHz for rural LTE/5G.  
o 3.5 GHz as the primary 5G band.  
Unique features:  
Large landmass → heavy reliance on low-band spectrum  for rural 
coverage.  
Early adoption of satellite augmentation  (Starlink, Inmarsat) to fill 
coverage gaps.  
 
5.8 Sub-THz and mmWave Challenges  
While mmWave (24–40 GHz) offers extreme bandwidth, coverage limitations  are 
significant:  
 Propagation Loss:  Free-space path loss increases 6 dB per frequency doubling.  
 Penetration Loss:  High absorption through walls, foliage, even rain.  
 Deployment Cost:  Requires dense small-cell infrastructure.  
ASEAN context: Dense cities like Singapore may benefit. Rural ASEAN less suitable. 
ANZ context: Limited mmWave beyond urban hotspots due to geography.  
Sub-THz for 6G introduces further challenges: atmospheric absorption, hardware cost, 
regulatory uncertainties.

Page 36

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 37 of 114  5.9 Spectrum Policy Trends  
 Harmonization:  ITU encourages global harmonization (e.g., 3.5 GHz for 5G), 
but national variations persist.  
 Dynamic Spectrum Sharing (DSS):  Used to allow LTE and 5G coexistence in 
same band.  
 Satellite-Terrestrial Convergence:  New ITU allocations for non-terrestrial 
networks (NTN)  under 5G NR.  
 AI for Spectrum Management:  Cognitive radio and ML-driven spectrum 
sensing proposed for 6G.  
 
5.10 Summary  
Spectrum regulation has been both an enabler and bottleneck of mobile technology 
evolution. The 900 MHz band  exemplifies the continuity of spectrum reuse, while 
higher bands (3.5 GHz, mmWave) reflect the demand for capacity. ASEAN’s 
fragmented policies contrast with ANZ’s coordinated planning, yet both regions 
demonstrate innovation in refarming, auctions, and satellite integration.  
AI and Data Science will increasingly be used for dynamic spectrum allocation, 
interference detection, and demand prediction , making spectrum management not 
only a regulatory challenge but also a computational one. 
 
 
 
 
 
 
 
 
 
 
 
END OF CHAPTER 5

Page 37

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 38 of 114

Page 38

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 39 of 114  Chapter 5.11  AI and ML for Propagation Prediction  
Traditional models are deterministic/empirical. AI/ML methods use real measurement 
data: 
 Supervised ML:  Train models on drive-test/KPI data to predict RSRP/RSRQ.  
 Deep Learning:  CNNs for RF fingerprinting.  
 Reinforcement Learning:  Adaptive beam alignment in mmWave links.  
 Big Data Analytics:  Correlating traffic patterns with propagation anomalies.  
Operators in Singapore and Australia have trialed AI for automatic coverage hole 
detection.  
 
Summary  
Radio propagation and link budgets are the foundation of mobile system design.  
Lower frequencies (e.g., 900 MHz) provide wide-area coverage, while higher 
frequencies (e.g., mmWave) require AI-optimized dense deployments. Modern 
operators cannot rely solely on mathematical models—they require data-driven, AI-
augmented approaches  to continuously optimize networks in real time.

Page 39

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 40 of 114  Chapter 6 – Modulation, Coding, and Multiple Access Evolution  
 
6.1 Introduction  
The evolution of modulation, coding, and multiple access techniques has been the 
engine driving improvements in capacity, spectral efficiency, and reliability  across 
successive mobile generations. Each leap in wireless standards has corresponded to 
advancements in how bits are represented on radio waves and how multiple users are 
multiplexed onto finite spectrum.  
This chapter explores these advancements chronologically, from analog FM in 1G  to 
OFDMA and advanced coding in 5G , while considering the growing role of AI and 
ML in waveform optimization, adaptive coding, and interference management.  
 
6.2 Modulation Evolution  
1G: Analog FM  
 Frequency Modulation (FM):  Simple but inefficient.  
 Channel bandwidth: 25–30 kHz.  
 Susceptible to interference, poor spectral efficiency.  
2G: Digital Modulation  
 GMSK (Gaussian Minimum Shift Keying)  in GSM.  
o Constant envelope → efficient power amplifiers.  
o Robust but limited spectral efficiency (~1 bit/s/Hz).  
 π/4-QPSK  in IS-54 TDMA.  
 QPSK/BPSK/CDMA spreading  in IS-95.  
2.5G and 3G  
 8-PSK in EDGE → 3 bits/symbol.  
 QPSK, 16-QAM, 64-QAM  in W-CDMA/HSPA.  
 Adaptive modulation introduced: switching based on channel conditions.

Page 40

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 41 of 114  4G LTE (Long  Term Evolution)  
 OFDMA (downlink):  Combats multipath with cyclic prefix.  
 SC-FDMA (uplink):  Reduces Peak-to-Average Power Ratio (PAPR).  
 Adaptive modulation up to 256-QAM.  
5G NR 
 Flexible numerologies (subcarrier spacing 15–240 kHz).  
 Higher-order QAM (up to 1024-QAM  in trials).  
 Beamformed modulation optimized for spatial channels.  
6G Outlook  
 Orbital Angular Momentum (OAM) waves  under research.  
 AI-designed waveforms:  Neural networks optimize modulation schemes 
dynamically.

Page 41

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 42 of 114  6.3 Channel Coding Evolution  
Error-correcting codes have evolved alongside modulation, crucial for reliability 
under fading and interference.  
 1G: No channel coding.  
 2G GSM:  Convolutional coding + interleaving.  
 3G UMTS:  Turbo codes → near Shannon-limit performance.  
 4G LTE:  Turbo codes (legacy), later LDPC (in trials).  
 5G NR: 
o LDPC (Low-Density Parity Check):  For data channels.  
o Polar Codes:  For control channels.  
 6G (Research):  Sparse graph codes, AI-assisted error correction.  
 
6.4 Multiple Access Techniques  
1G – FDMA – Frequency Divided Multiple Access Technology  
 Users separated by frequency.  
 Simple but inefficient, poor scalability.  
2G – TDMA & CDMA – Time Divided & Code Division Multiple-Access  
 TDMA (Time Division Multiple Access):  
o GSM: 200 kHz channels, 8 time slots.  
o Effective for circuit-switched voice.  
 CDMA (Code Division Multiple Access):  
o IS-95, UMTS.  
o Spreads signals with pseudo-random codes.  
o Soft handoffs, interference-limited planning.

Page 42

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 43 of 114  3G – W-CDMA  
 Wideband CDMA, 5 MHz carriers.  
 Supported higher data rates but suffered from self-interference.  
OFDMA  
 Orthogonal Frequency Division Multiple Access.  
 Divides spectrum into orthogonal subcarriers.  
 Flexible scheduling in time–frequency grid.  
 Multipath resilience, high spectral efficiency.  
5G NR – Flexible OFDM  
 Variable subcarrier spacing.  
 OFDM extended to mmWave, massive MIMO.  
 Sidelink and non-terrestrial integration.  
 Support for URLLC (low latency) , mMTC (IoT) , eMBB (broadband).  
6G Outlook  
 NOMA (Non-Orthogonal Multiple Access):  
o Users multiplexed by power domain.  
o ML used for successive interference cancellation.  
 Rate-Splitting Multiple Access (RSMA).  
 AI-driven resource allocation predicted to dominate.

Page 43

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 44 of 114  6.5 Synchronization and Reference Signals  
Modern systems require precise synchronization  for coherent detection.  
 2G GSM:  Broadcast Control Channel (BCCH).  
 3G: Primary/Secondary Synchronization Channels.  
 4G LTE:  Primary/Secondary Sync Signals, Cell-specific Reference Signals.  
 5G NR: 
o PSS/SSS for cell search.  
o DM-RS/CSI-RS  for channel estimation in beamformed channels.  
AI-enhanced synchronization techniques are being explored, where ML algorithms 
dynamically track Doppler and fading.  
 
6.6 AI and Data Science in Modulation and Coding  
 Adaptive Modulation & Coding (AMC):  Traditional link adaptation uses 
SNR  (Signal Noise Ratio) thresholds. AI-based methods optimize multi-
dimensional trade-offs  (throughput, reliability, latency).  
 Deep Learning for Decoding:  Neural decoders outperform LDPC in some 
cases. 
 Reinforcement Learning for Multiple Access:  Used for NOMA power 
allocation.  
 Waveform Design:  AI models search modulation/coding combinations beyond 
human-designed standards.

Page 44

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 45 of 114  6.7 Summary  
From simple analog FM (1G)  to AI-optimized OFDM and coding in 5G and 
beyond, modulation and multiple access have been central to increasing capacity and 
efficiency. While human-designed techniques dominated until 5G, the 6G era will 
increasingly rely on AI to co-design and optimize waveforms, coding, and access 
schemes in real time. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
END OF CHAPTER 6

Page 45

7.1 Introduction
A mobile network is not only defined by its air interface or modulation scheme but also
by its topology — the physical and logical arrangement of base stations, cells, and
user devices. Over time, as demand for coverage, capacity, and reliability has
increased, mobile networks have evolved from simple macrocellular deployments to
heterogeneous networks (HetNets) integrating macro, micro, pico, femto cells,
mesh, and device-to-device (D2D) topologies.
In parallel, Low-Power Wide Area Networks (LPWANs) such as LoRaWAN
introduced a different topology optimized for low data rate, long-range IoT
connectivity.
This chapter examines the evolution of topologies, their trade-offs, and the role of AI
and data science in self-organizing networks (SONs) and autonomous topology
management.

7.2 Macrocellular Topology
Definition
Macrocells are large coverage areas served by high-power
base stations, typically transmitting at 20–40 W, mounted on
towers or rooftops.
Characteristics
 Coverage radius: 2–35 km depending on frequency
and terrain.
 Primary layer of 1G–4G deployments.
 Cost-effective for rural areas (ANZ’s vast geography).
Limitations
 Inefficient for indoor coverage in dense urban environments.
 High inter-cell interference in congested networks.

Page 46

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 47 of 114  7.3 Microcells  
Definition  
Smaller cells (100 m – 2 km radius) deployed for capacity enhancement  
in high-traffic areas.  
Use Cases  
 Urban hotspots (shopping malls, downtown cores).  
 Supplement macro coverage in ASEAN megacities like 
Bangkok, Manila, Jakarta.  
Trade-offs  
 Lower transmit power → lower CAPEX per site.  
 Increased OPEX due to denser site requirements.

Page 47

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 48 of 114  6.4 Picocells 
Definition  
Very small base stations with coverage radius of 10–200 meters.  
Use Cases  
 Enterprises, office buildings, airports.  
 Often used for indoor LTE/5G private networks.  
ASEAN/ANZ Deployment  
 Singapore uses picocells for indoor 5G coverage in MRT 
tunnels. 
 Australia: mining companies deploy picocells for private LTE in remote 
facilities.  
 
6.5 Femtocells  
Definition  
Ultra-small, consumer-deployed base stations (coverage radius 10–50 m).  
Role 
 Extend indoor coverage.  
 Use residential broadband as backhaul.  
Challenges  
 Interference management.  
 Security of user-deployed nodes.  
 
6.6 Heterogeneous Networks (HetNets)  
HetNets combine macro, micro, pico, and femto cells  into a layered architecture.  
Benefits 
 Macro layer ensures coverage.  
 Small cells increase localized capacity.  
 Offloading traffic improves spectrum efficiency.

Page 48

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 49 of 114  6.7 SON (Self-Organizing Networks)  
 Automates configuration and optimization.  
 AI-based SONs adjust neighbor relations, power levels, and handover 
thresholds dynamically.  
 Widely used in 4G/5G deployments in ANZ (Telstra’s AI-driven SON pilot).  
6.8 Mesh and Ad Hoc Networks  
Public Safety and Tactical Use  
 TETRA systems use mesh/ad hoc extensions for first responders. (Used in 
Singapore for Oil & Gas, specific to Bukom & Jurong Islands)  
 ProSe (Proximity Services):  Introduced in LTE Rel-12 for device-to-device.  
Military and Industrial IoT  
 Mesh networks in mining (Australia) and oil rigs (Malaysia).  
 D2D communication critical for autonomous vehicles.  
Challenges  
 Interference and scalability.  
 Security of decentralized topologies.  
6.9 LPWAN Topologies 
LoRaWAN – Low power Long Range Wide Area Network  
Star-of-stars  topology: end devices → gateways → network server.  
Gateways connect to central servers via IP backhaul.  
Optimized for long-range, low-power IoT telemetry.  
Alternatives  
 Sigfox: Ultra-narrowband star topology.  
 NB-IoT / LTE-M:  Cellular LPWAN alternatives.  
ASEAN/ANZ Adoption  
Singapore: nationwide LoRaWAN IoT deployments for smart city.  
New Zealand: farmers use LoRaWAN for livestock monitoring.

Page 49

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 50 of 114  6.10 AI/ML in Topology Optimization 
Cell Planning  
 AI models trained on traffic data predict where small cells should be deployed.  
 Reinforcement learning for automatic tilt optimization  of antennas.  
Traffic Load Balancing  
 Clustering algorithms (k-means, DBSCAN) detect overloaded cells.  
 Load shifted via AI-controlled handover tuning.  
Failure Recovery  
 Predictive maintenance using anomaly detection.  
 Self-healing topologies reroute traffic around failed nodes.

Page 50

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 51 of 114   
6.11 Comparative Topology Trade-offs 
Topology  Coverage 
Radius Strengths  Limitations  
Macrocell  2–35 km  Wide area coverage, cost-
effective  Poor indoor 
penetration  
Microcell  0.1–2 km  Capacity boost, urban use  Higher site density  
Picocell 10–200 m  Indoor coverage, enterprise  CAPEX for enterprise  
Femtocell  <50 m Consumer indoor use  Security, interference  
Mesh/Ad 
Hoc Varies Resilient, decentralized  Complexity, 
interference

Page 51

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 52 of 114  6.12 Summary 
Network topologies evolved from macro-only 1G deployments  to sophisticated 
multi-layered 5G HetNets  and specialized IoT topologies. In ASEAN, dense cities 
require pico/femto deployments , while ANZ emphasizes macrocells and satellite 
integration.  
AI and Data Science enable dynamic optimization of these topologies, ensuring real-
time adaptability to changing traffic, failures, and propagation environments. 
 
 
END OF CHAPTER 6

Page 52

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 53 of 114   
Chapter 7 - Standards by Generation  
(1G to 5G, Wi-Fi, WiMAX, LoRaWAN)  
 
7.1 Introduction  
Telecommunications systems evolve not in isolation but under the guidance of 
standards bodies , which ensure interoperability, economies of scale, and global 
compatibility.  The most influential standards bodies are:  
 3GPP (3rd Generation Partnership Project):  LTE, 5G NR, core networks.  
 ITU (International Telecommunication Union):  Defines IMT-2000, IMT-
Advanced, IMT-2020 (frameworks for 3G, 4G, 5G).  
 IEEE (Institute of Electrical and Electronics Engineers):  Wi-Fi (802.11), 
WiMAX (802.16).  
 LoRa Alliance:  LoRaWAN standardization for LPWAN.  
This chapter provides a generation-by-generation breakdown of standards, followed by 
Wi-Fi/WiMAX and LoRaWAN ecosystems.  
 
7.2 1G Standards (Analog Cellular) 
AMPS (Advanced Mobile Phone System)  
 Launched in the US in 1983.  
 30 kHz FM channels in the 800 MHz band.  
 FDMA, no encryption, poor spectral efficiency.  
NMT (Nordic Mobile Telephony) – NoKIA/NorTEL  
 First international cellular standard (Scandinavia, early 1980s).  
 450/900 MHz bands.  
 Influenced global adoption.

Page 53

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 54 of 114  TACS (Total Access Communication System)  
 UK adaptation of AMPS.  
 Widely used in Europe and Asia before GSM.  
Summary:  1G standards were fragmented, regional, and analog-only.

Page 54

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 55 of 114  7.3  2G Standards (Digital Cellular) 
GSM (Global System for Mobile Communications)  
 ETSI standard (Europe, 1991).  
 TDMA, 200 kHz carriers.  
 Global adoption, especially in ASEAN/ANZ.  
 Introduced SMS and encryption.  
IS-95 (cdmaOne)  
 Qualcomm’s CDMA-based system.  
 Spread spectrum, soft handoffs.  
 Popular in US, Korea.  
IS-136 (D-AMPS)  
 US TDMA system.  
 Transitional, eventually replaced by GSM/CDMA.  
2.5G Enhancements  
 GPRS: Packet-switched data (~50 kbps).  
 EDGE: Enhanced modulation (8-PSK), up to ~200 kbps.

Page 55

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 56 of 114  7.4 3G Standards (IMT-2000) 
UMTS / W-CDMA  
 Europe and Asia.  
 5 MHz carriers, 2.1 GHz band.  
 HSPA/HSPA+ → up to 42 Mbps.  
CDMA2000  
 Evolution of IS-95.  
 1xRTT, EV-DO.  
 Deployed in North America, some Asian markets.  
Regional Adoption  
 ASEAN: uneven rollout due to cost.  
 ANZ: Telstra’s “Next G” (850 MHz) was one of the most advanced UMTS 
networks globally.

Page 56

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 57 of 114  7.5  4G Standards (IMT-Advanced)  
LTE (Long Term Evolution)  
 3GPP Release 8 (2009).  
 OFDMA (DL), SC-FDMA (UL).  
 Up to 20 MHz per carrier; carrier aggregation (CA) enabled multi-carrier 
operation.  
 LTE-Advanced (Rel-10+):  
o Higher-order MIMO (8x8).  
o Coordinated multipoint (CoMP).  
o Peak rates >1 Gbps.  
WiMAX (IEEE 802.16e/m)  
 Considered 4G by ITU, but lost to LTE due to ecosystem dominance.  
 Still deployed in some ASEAN regions for fixed broadband.  
 
7.6 5G Standards (IMT-2020) 
3GPP Release 15–17  
 5G NR (New Radio).  
 Flexible numerology (15–240 kHz spacing).  
 Sub-6 GHz (FR1) and mmWave (FR2).  
 Massive MIMO, beamforming.  
 NSA (non-standalone, LTE anchor) and SA (standalone, 5G core).  
Release 18 and 5G Advanced  
 AI-native RAN.  
 Enhanced XR (Extended Reality) support.  
 RedCap (Reduced Capability) devices for IoT.  
 Non-terrestrial networks (NTN): satellites integrated with 5G.

Page 57

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 58 of 114   
7.7 Wi-Fi Standards (IEEE 802.11 Family)  
 
Generation  Standard  Year Features  
Wi-Fi 1 802.11b 1999 DSSS, 2.4 GHz, 11 Mbps  
Wi-Fi 2 802.11a 1999 OFDM, 5 GHz, 54 Mbps  
Wi-Fi 3 802.11g 2003 2.4 GHz, OFDM, 54 Mbps  
Wi-Fi 4 802.11n 2009 MIMO, 2.4/5 GHz, 600 Mbps  
Wi-Fi 5 802.11ac  2014 MU-MIMO, 160 MHz channels, Gb/s  
Wi-Fi 6 802.11ax  2019 OFDMA, BSS Coloring, low latency  
Wi-Fi 7 802.11be  2024 
(ongoing)  320 MHz channels, multi- link operation 
(MLO) 
 
 
ASEAN/ANZ: Wi-Fi complements cellular for indoor connectivity; Wi-Fi 6E in 6 GHz 
is adopted in Australia and Singapore.  
 
LoRaWAN (Low-Power Wide Area Network) 
Characteristics  
 Physical Layer:  LoRa modulation (chirp spread spectrum).  
 Topology:  Star-of-stars (end devices → gateways → network servers).  
 Bands: ISM (868/915 MHz in ANZ/ASEAN).  
Device Classes  
 Class A: Ultra-low power, scheduled downlinks.  
 Class B: Periodic beacon windows.  
 Class C: Almost continuous receive.  
Deployment  
 ASEAN: Smart city (Singapore, Malaysia).  
 ANZ: Agricultural IoT (livestock, irrigation monitoring).

Page 58

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 59 of 114   
7.8 Comparative Technology Matrix  
 
Technology  Access Method  Peak Data 
Rate Latency Strengths  Limitations  
GSM/EDGE  TDMA ~0.2 Mbps  ~150 ms  Wide coverage  Low data rates  
UMTS/HSPA  W-CDMA  14–42 Mbps  50–100 
ms Proven tech  Interference mgmt  
LTE-A OFDMA/SC-
FDMA >1 Gbps 20–50 ms  Ecosystem, efficiency  Dense planning  
5G NR OFDMA  >10 Gbps  <1 ms Flexible, slicing, 
URLLC Complexity, mmWave 
limits 
Wi-Fi 6/7  OFDMA  Multi-Gbps  ~10 ms Unlicensed, cheap  Contention, interference  
LoRaWAN  ALOHA-like  kbps Seconds Long range, ultra-low 
power Very low throughput  
 
7.9 Summary 
 
Standards have progressed from analog fragmentation (1G)  to globally harmonized 
digital ecosystems (5G NR).  Wi-Fi and LoRaWAN evolved in parallel, targeting 
different niches: Wi-Fi for indoor broadband  and LoRaWAN for ultra-low power 
IoT. 
The unifying trend is toward flexible, AI-native, software-defined standards  where 
modulation, access, and resource allocation can adapt in real time. This lays the 
groundwork for 6G, where AI will likely play a direct role in waveform and standard 
evolution.  
END OF CHAPTER 7

Page 59

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 60 of 114   
Chapter 8 - Specialized Systems & Use Cases  
(LoRaWAN, TETRA ATEX, ST GRID, Satellites: Beidou, Gouwang, Qianfan, Starlink, 
Inmarsat)  
 
8.1 Introduction  
Beyond mainstream mobile standards (1G–5G), specialized wireless systems have 
emerged to meet unique requirements : 
 Low-power IoT (LoRaWAN).  
 Mission-critical communications (TETRA ATEX, ST GRID).  
 Satellite-based systems (Beidou, Gouwang, Qianfan, Starlink, Inmarsat).  
These systems fill niches not well served by cellular broadband: industrial safety, 
rural connectivity, global positioning, maritime/aviation communications, and 
massive IoT telemetry.  
 
8.2 LoRaWAN (Low Power, Long Range IoT)  
Technical Overview  
 LoRa (physical layer):  Chirp spread spectrum modulation.  
 LoRaWAN (protocol):  Defines MAC and backend.  
 Bands: ISM (EU 868 MHz, US/AU 915 MHz, ASEAN varies).  
 Data rate:  0.3–50 kbps.  
 Range: 2–15 km urban, >50 km rural line-of-sight.  
 Battery life:  5–10 years.  
Use Cases  
 Smart cities: parking sensors, waste management.  
 Agriculture: soil moisture, livestock tracking (ANZ).  
 Disaster response: temporary sensor networks.  
Pros and Cons  
 ࿨࿩࿪ Long range, ultra-low power, unlicensed spectrum. 
 ᤶᤷ Low throughput, high latency (seconds).

Page 60

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 61 of 114  8.3 TETRA ATEX Systems  
 
TETRA (Terrestrial Trunked Radio)  
 ETSI standard for professional mobile radio.  
 Used by police, emergency services, utilities.  
 Digital trunking, encryption, direct mode (D2D).  
 446Mhz in Singapore  
ATEX Compliance  
 Devices certified for explosive environments (oil rigs, chemical 
plants, mining).  
 Ruggedized walkie-talkies with intrinsically safe design.  
ASEAN/ANZ Deployment  
 ASEAN: TETRA used in airports, seaports, public safety 
(Singapore Police, Jakarta MRT).  
 ANZ: ATEX radios in mining (Western Australia, Queensland 
coal sector).  
 
Pros and Cons  
 
 ࿨࿩࿪ Mission-critical reliability, D2D, encrypted. 
 ᤶᤷ Limited bandwidth, not suited for broadband data. Transmitter is ranged up to 10km 
radius

Page 61

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 62 of 114  8.4 -  ST GRID Walkie-Talkie Phone 
 
 
Overview  
 Hybrid of mobile phone and push-to-talk 
(PTT) radio.  
 Developed for industrial/public safety 
environments.  
 Operates on both cellular and dedicated PMR spectrum. 800/850x Mhz Freq in 
Singapore  
Features  
 Group calls with near-zero latency.  
 Ruggedized design.  
 Integration with LTE mission-critical push-to-talk (MCPTT).  
ASEAN/ANZ Use Cases  
 ANZ: Utilities and railway operators use ST GRID devices for workforce 
communication.  
 ASEAN: Oil & gas industry, metro projects (e.g., Malaysia’s MRT).  
Pros and Cons  
 ࿨࿩࿪ Instant communication, hybrid cellular/PMR. 
 ᤶᤷ Niche, not scalable for broadband IoT.

Page 62

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 63 of 114  8.5 Satellite Systems 
 
Beidou (China)  
 Global Navigation Satellite System (GNSS).  
 Provides positioning, navigation, timing (PNT).  
 Coverage: Global since 2020 (BDS-3).  
 ASEAN impact: integrated into agriculture and 
logistics.  
Gouwang (China’s broadband LEO project)  
 LEO constellation under development.  
 Target: broadband satellite internet, competitor to Starlink.  
Qianfan  
 Another Chinese LEO initiative for global internet coverage.  
 Focus on integration with Belt & Road countries, including ASEAN.  
Starlink (SpaceX, USA)  
 
 LEO constellation (>5,000 satellites deployed as of 2024).  
 Low latency (~25–40 ms).  
 Broadband speeds 50–250 Mbps.  
 ASEAN: Pilots in Philippines, Malaysia.  
 ANZ: Widely used in rural Australia/New Zealand for broadband.  
Inmarsat (UK)  
 Legacy GEO satellite operator.  
 Provides maritime, aviation, emergency communications.  
 Lower data rates, higher latency (~600 ms).

Page 63

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 64 of 114   
 
 
  System Orbit/Layer Latency Strengths Limitations 
Beidou MEO/GEO ~50–100 ms  GNSS PNT Not broadband 
Gouwang/Qianfan LEO (planned)  ~25–40 ms National broadband 
strategy Still unproven 
Starlink LEO 25–40 ms Broadband, rural coverage Expensive, high 
CAPEX 
Inmarsat GEO ~600 ms Aviation, maritime High latency, 
limited throughput  
LTE/5G Terrestrial 1–20 ms High capacity Limited rural 
reach 
LoRaWAN Terrestrial ISM  Seconds Ultra-low power IoT Very low 
throughput 
TETRA ATEX PMR <100 ms Mission-critical voice No broadband

Page 64

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 65 of 114  8.6 Comparative Analysis: Satellite vs Terrestrial  
 
AI/ML in Specialized Systems  
 LoRaWAN:  ML-based anomaly detection in IoT sensor data.  
 TETRA ATEX:  AI-based predictive maintenance of mission-critical radios.  
 Satellites:  
o Beam-hopping optimization using AI.  
o Traffic load prediction in LEO constellations.  
o AI-based GNSS error correction (multipath mitigation).  
 
ASEAN and ANZ Regional Use Cases  
 ASEAN:  
o Singapore: LoRaWAN smart nation projects.  
o Philippines: Starlink trial for rural schools.  
o Indonesia: Beidou adoption in logistics/shipping.  
 ANZ: 
o Australia: Starlink integrated into Telstra partnerships.  
o New Zealand: LoRaWAN for agricultural IoT.  
o Mining/oil industries: ATEX walkie-talkies.

Page 65

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 66 of 114  8.7 Summary  
Specialized systems illustrate the breadth of wireless applications beyond 
mainstream 5G.  
 LoRaWAN  dominates low-power IoT.  
 TETRA ATEX / ST GRID  serve mission-critical industrial/public safety 
needs. 
 Satellites (Beidou, Starlink, Inmarsat)  extend coverage where terrestrial fails.  
Together, they form an ecosystem of complementary technologies . AI/ML ensures 
their integration into a seamless connectivity fabric for ASEAN and ANZ. 
 
END CHAPTER 8

Page 66

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 67 of 114   
Chapter 9  - Core Networks: From MSC to 5GC with AI 
Enhancements  
 
9.1 Introduction  
While the Radio Access Network (RAN)  defines how users connect to the network, 
the core network  provides intelligence, switching, and control that enable mobility, 
authentication, and services. Core networks have evolved from circuit-switched 
telephony systems in 1G/2G  to the all-IP, cloud-native, service-based architecture 
(SBA) of 5G.  
This chapter explores core network evolution, with emphasis on:  
 MSC (Mobile Switching Centers) in 1G/2G.  
 SGSN/GGSN in 3G.  
 EPC (Evolved Packet Core) in LTE.  
 5GC (5G Core), cloud-native and AI-enabled.  
We also highlight AI, data science, and ML integration  in traffic engineering, 
security, and self-healing.

Page 67

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 68 of 114  9.2 1G/2G Core Networks – Circuit Switching  
Mobile Switching Center (MSC)  
 Centralized circuit switch.  
 Handles call setup, routing, and handovers.  
 Connected to PSTN for voice services.  
Home/Visitor Location Registers (HLR/VLR)  
 HLR: Stores subscriber identity and authentication keys.  
 VLR: Temporary database for roaming subscribers.  
Authentication Center (AuC)  
 Stores secret keys for SIM-based authentication.  
Short Message Service Center (SMSC)  
 Enabled SMS delivery.  
Limitations:  No packet data, scalability issues, inflexible.  
 
 
9.3 3G Core Networks – Dual Circuit and Packet  
 
Serving GPRS Support Node (SGSN)  
 Routes data packets.  
 Tracks mobility for packet-switched services.  
Gateway GPRS Support Node (GGSN)  
 Connects packet data traffic to external networks (e.g., internet).  
Circuit/Packet Split  
 Dual domains coexisted: circuit (voice) + packet (data).  
Impact: First step toward always-on data connections, though still limited compared to 
LTE.

Page 68

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 69 of 114  9.4 4G Core Networks – The Evolved Packet Core (EPC)  
EPC Architecture  
 MME (Mobility Management Entity):  Controls mobility, authentication.  
 SGW (Serving Gateway):  Routes user traffic.  
 PGW (Packet Gateway):  Connects to external IP networks.  
 PCRF (Policy and Charging Rules Function):  Manages QoS and charging  
All-IP Architecture  
 Voice over LTE (VoLTE) through IMS (IP Multimedia Subsystem).  
 Circuit switching eliminated.  
Benefits 
 Reduced latency.  
 Scalability for broadband.  
 Network sharing and virtualization enabled.  
 
9.5G Core (5GC) – Service-Based Architecture  
Service-Based Architecture (SBA)  
 Components exposed as services via APIs.  
 Cloud-native, microservices architecture.  
Key Functions  
 AMF (Access and Mobility Management Function):  Manages user access 
and mobility.  
 SMF (Session Management Function):  Session setup, QoS policies.  
 UPF (User Plane Function):  Handles user traffic forwarding.  
 NRF (Network Repository Function):  Service discovery.  
 PCF (Policy Control Function):  Policy management.  
 AUSF (Authentication Server Function):  Authentication and security.

Page 69

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 70 of 114  Features  
 Network Slicing:  Logical partitioning for different services (e.g., URLLC vs 
eMBB). 
 Edge Computing (MEC):  Applications hosted at the edge to reduce latency.  
 Non-Terrestrial Networks (NTN):  Satellites integrated with 5GC.

Page 70

© AZRIN.INFO 2025
UNIVERSITA’ DEGLI GUGLIELMO MARCONI Page 71 of 114 9.6 Virtualization and Cloud-Native Cores
 NFV (Network Function Virtualization): Replaces proprietary hardware with
virtualized functions.
 SDN (Software Defined Networking): Separates control/data planes, enabling
programmability.
 Containers and Kubernetes: Used in 5GC for cloud-native deployments.

9.7 AI and Data Science in Core Networks
7.7.1 Traffic Prediction and Optimization
 ML models forecast traffic load, enabling dynamic scaling of UPFs.
 Time-series models (ARIMA, LSTM) predict busy-hour congestion.
7.7.2 Network Slicing Management
 AI orchestrators dynamically allocate slices.
 Reinforcement learning optimizes slice performance.
7.7.3 Security and Anomaly Detection
 ML-based intrusion detection identifies unusual traffic patterns.
 AI-based fraud prevention (SIM cloning, DDoS mitigation).
7.7.4 Fault Management and Self-Healing
 Predictive maintenance of virtual network functions (VNFs).
 AI-based failure prediction in data centers.

9.8 ASEAN and ANZ Deployment Trends
ASEAN
 Singapore: early adoption of 5GC standalone networks.
 Malaysia: wholesale 5G model integrates 5GC for multiple operators.
 Indonesia/Philippines: NSA rollouts dominate, limited standalone due to cost.
ANZ
 Australia: Telstra deploying cloud-native 5GC with edge computing for
industrial IoT.
 New Zealand: Spark focusing on MEC for smart agriculture.

Page 71

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 72 of 114   
9.9 Summary  
The core network has evolved from rigid circuit-switching (MSC)  to flexible, AI-
native service-based architectures (5GC).  
 1G/2G: voice-centric, circuit-switched.  
 3G: dual circuit + packet.  
 4G: all-IP EPC.  
 5G: service-based, virtualized, sliceable, AI-enabled.  
Future 6G cores  will be autonomous, cognitive, and self-optimizing , with AI not as 
an overlay but as a native capability. 
 
END CHAPTER 9

Page 72

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 73 of 114   
Chapter 10 - Security and Authentication Across Generations  
 
10.1 Introduction  
Security in mobile networks has evolved alongside the technologies themselves. While 
1G networks offered virtually no protection against eavesdropping and fraud , 
today’s 5G networks provide end-to-end encryption, mutual authentication, and 
subscriber privacy mechanisms.  
However, each generation introduced new attack surfaces : cloning in 1G, weak 
ciphers in 2G, signaling storms in 3G, IMS vulnerabilities in 4G, and virtualized 
network security risks in 5G. Looking ahead, AI/ML-enhanced security frameworks  
are expected to underpin 6G.  
 
10.2 1G: Insecure Beginnings  
 Analog FM voice:  No encryption, easily intercepted by scanners.  
 Authentication:  None; phone cloning rampant (copying ESN/IMSI).  
 Vulnerabilities:  
o Fraudulent calls billed to victims.  
o Eavesdropping in sensitive scenarios (e.g., law enforcement).  
 
10.3 2G: Basic Digital Security  
Authentication and Encryption  
 SIM-based authentication:  Introduced secret key (Ki) stored in SIM and 
AuC. 
 A3/A8 algorithms:  Generate session key (Kc).  
 Ciphering:  A5/1, A5/2 stream ciphers.  
Weaknesses  
 A5/1 eventually cracked with rainbow tables.  
 No mutual authentication (network not authenticated by handset) → man-
in-the-middle attacks.

Page 73

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 74 of 114  10.4 3G: Stronger Mutual Authentication  
UMTS Authentication and Key Agreement (AKA)  
 Mutual authentication  introduced.  
 128-bit keys.  
 Stronger algorithms (Kasumi cipher).  
Security Gains  
 Protected against fake base stations.  
 Improved integrity protection.  
Limitations  
 Still vulnerable to denial-of-service and jamming.  
 Signaling overload attacks began to emerge.  
 
10.5 4G: IP-Based Security Challenges  
EPS-AKA  
 Mutual authentication maintained.  
 Session keys for encryption/integrity on IP bearer.  
VoLTE and IMS Vulnerabilities  
 SIP protocol in IMS exposed to spoofing, denial-of-service.  
 Interworking with internet services increased attack surface.  
LTE-Specific Threats  
 IMSI Catchers (Stingrays):  Exploited fallback to 2G/3G for interception.  
 Rogue eNodeBs:  Fake base stations used for surveillance.

Page 74

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 75 of 114  10.6 5G: Enhanced Security and Privacy  
5G Authentication and Key Agreement (5G-AKA)  
 Based on EPS-AKA but enhanced.  
 Introduced SUCI (Subscription Concealed Identifier)  to protect IMSI.  
 Home network authenticates user directly, reducing reliance on roaming 
operator.  
Integrity Protection  
 Extended to user plane as well as control plane.  
Network Slicing Security  
 Each slice may have independent security policies.  
 AI used for adaptive policy enforcement.  
Virtualization and Cloud Risks  
 NFV/SDN components exposed to cloud vulnerabilities.  
 Zero-trust architectures emerging.  
 
10.7 AI and Data Science for Security 
Anomaly Detection  
 ML models trained on traffic patterns detect botnets, DDoS, or malware.  
 Supervised and unsupervised learning for intrusion detection.  
Threat Prediction  
 Predictive analytics flag unusual signaling storms.  
 Reinforcement learning enhances adaptive firewalls.  
Privacy-Preserving AI  
 Federated learning trains security models without centralizing sensitive 
data.

Page 75

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 76 of 114  10.8 ASEAN and ANZ Security Considerations 
ASEAN 
 Some ASEAN countries still operate 2G → vulnerabilities remain.  
 Increased risk from state and non-state actors exploiting outdated protocols.  
 Singapore leading in 5G cybersecurity regulation.  
ANZ 
 Australia implemented 5G security bans  on certain vendors citing supply 
chain concerns.  
 New Zealand adopting cloud security frameworks for 5GC deployments.  
 
10.9 Comparative Evolution of Security  
Generation  Auth Method  Encryption  Weaknesses  
1G None None Cloning, eavesdropping  
2G SIM-based (A3/A8)  A5/1, A5/2  No mutual auth, weak ciphers  
3G AKA Kasumi DoS, jamming  
4G EPS-AKA  AES/Snow3G  IMS vulns, rogue eNodeB  
5G 5G-AKA  256-bit AES, integrity  NFV/SDN risks, slice complexity

Page 76

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 77 of 114  10.10 Summary  
Security has advanced from non-existent (1G)  to mutual, encrypted, privacy-
preserving (5G).  Yet, vulnerabilities evolve with technology: virtualized, cloud-
native 5G cores create new risks.  AI and ML are indispensable in threat detection, 
anomaly analysis, and adaptive security.  
In ASEAN/ANZ, the coexistence of legacy 2G  with cutting-edge 5G  creates unique 
regional security challenges. 
 
END CHAPTER 10

Page 77

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 78 of 114  Chapter 11 – Performance, QoS, and KPI Engineering  
 
11.1 Introduction  
Performance engineering ensures that mobile and wireless networks meet user 
expectations and service-level agreements (SLAs).  
This involves measuring Key Performance Indicators (KPIs) , enforcing Quality of 
Service (QoS) classes , and optimizing resources to balance coverage, capacity, and 
cost. 
With the advent of AI and big data analytics, performance management has shifted from 
reactive monitoring  to predictive, self-optimizing systems.  
 
11.2 Fundamental Performance Metrics  
11.2.1 Coverage Metrics  
RSRP (Reference Signal Received Power):  Measures coverage strength.  
RSRQ (Reference Signal Received Quality):  Signal quality relative to 
interference.  
SINR (Signal-to-Interference-plus-Noise Ratio):  Key for throughput 
estimation.  
11.2.2 Capacity Metrics  
Cell throughput (DL/UL).  
Spectral efficiency (bps/Hz).  
Number of active users per cell.  
11.2.3 Reliability Metrics  
Block Error Rate (BLER).  
Call Drop Rate.  
Packet Loss.

Page 78

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 79 of 114  11.2.4 Latency and Jitter  
Critical for real-time applications (VoIP, gaming, URLLC).  
11.3 QoS Frameworks  
11.3.1 2G/3G Era  
Best-effort packet data in GPRS.  
Circuit-switched QoS for voice.  
11.3.2 4G LTE  
QoS Class Identifier (QCI):  Defines priority, delay, packet loss.  
Differentiates services (e.g., VoLTE vs video streaming).  
11.3.3 5G NR  
 5QI (5G QoS Identifier):  Flexible, service-specific.  
Example:  
o URLLC:  ultra-low latency, high reliability.  
o eMBB: high throughput.  
o mMTC: massive IoT connections.  
11.4 KPI Engineering  
9.4.1 Radio Access KPIs  
Handover success rate.  
RRC (Radio Resource Control) setup success.  
PRB (Physical Resource Block) utilization.  
11.4.2 Core Network KPIs  
Session setup success rate.  
Mean session duration.  
Signaling load distribution.

Page 79

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 80 of 114  11.4.3 Service KPIs  
VoLTE MOS (Mean Opinion Score).  
Video start time, buffering ratio.  
IoT device availability.  
11.5 AI and Data Science for Performance Optimization  
11.5.1 Predictive Analytics  
ML models (LSTM, ARIMA) forecast traffic loads.  
Seasonal patterns detected in ASEAN/ANZ (e.g., urban rush hour vs rural IoT).  
      11.5.2 Root Cause Analysis  
Data clustering isolates fault patterns.  
AI distinguishes between hardware failure vs interference vs misconfiguration.  
11.5.3 Self-Optimizing Networks (SON)  
Dynamic PCI allocation.  
Automatic load balancing.  
AI-based antenna tilt and power optimization.  
11.5.4 QoE (Quality of Experience) Prediction  
Data fusion: combine KPI data with crowdsourced app-level metrics.  
Example: Telstra’s ML-driven app experience scoring.

Page 80

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 81 of 114  11.6 ASEAN and ANZ Deployment Context  
11.6.1 ASEAN  
Dense cities (Jakarta, Manila, Bangkok): KPI engineering focused on congestion 
and interference mitigation.  
Smart city rollouts (Singapore): QoS tuned for IoT and AR/VR applications.  
11.6.2 ANZ  
Rural coverage: KPIs focused on coverage continuity and satellite integration.  
Mining/industrial IoT: SLAs prioritize machine telemetry reliability over 
throughput.  
11.7 Example KPI Targets (5G in ASEAN/ANZ) 
 
KPI Target (Urban)  Target (Rural)  
DL throughput  1 Gbps+ 100 Mbps+  
Latency <10 ms <20 ms 
Handover success  >99% >97% 
Call drop rate  <0.5% <1% 
IoT availability  99.9% 99% 
 
11.8 Case Studies  
11.8.1 Singapore  
AI-enhanced KPI monitoring in 5G standalone networks.  
Network slicing deployed for autonomous vehicle trials.  
11.8.2 Australia  
Telstra using AI to balance capacity between urban eMBB  and rural fixed 
wireless access (FWA).  
Integration with Starlink for outback connectivity.

Page 81

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 82 of 114  11.9 Challenges  
 Data Overload:  Billions of KPI data points daily → big data processing 
required.  
 Heterogeneity:  LTE/5G/LoRaWAN coexistence complicates KPIs.  
 QoE Alignment:  User experience often diverges from network-centric KPIs.  
 Security Risks:  Exposing KPI data pipelines may create vulnerabilities.  
 
11.10 Summary  
Performance engineering evolved from basic voice KPIs in 2G  to multi-layered AI-
driven frameworks in 5G.  
 2G/3G: best-effort packet data.  
 4G: structured QoS via QCIs.  
 5G: ultra-flexible QoS via 5QIs, AI-driven KPI monitoring.  
ASEAN and ANZ deployments highlight contrasting needs: urban congestion vs 
rural coverage.  AI and ML are now indispensable for ensuring QoE, optimizing 
KPIs, and predicting failures. 
 
 
 
END OF CHAPTER 11

Page 82

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 83 of 114  Chapter 12 – Deployment, Optimization, and Testing  
 
12.1 Introduction  
Building a mobile or wireless system is not just about spectrum and standards; 
deployment and optimization  are the decisive factors that determine whether a 
network meets expectations.  
From site acquisition and radio planning  to drive testing and AI-assisted 
optimization , network engineering requires a structured methodology. With 5G and 
beyond, automation and data-driven intelligence dominate, reducing reliance on manual 
fieldwork.  
 
12.2 Deployment Fundamentals  
12.2.1 Site Selection  
Coverage-driven (rural ANZ):  Maximize cell radius, fewer sites.  
Capacity-driven (urban ASEAN):  Densify with small cells and HetNets.  
Consider zoning, environmental impact, and backhaul availability.  
12.2.2 Spectrum Planning  
Lower bands (<1 GHz): coverage.  
Mid-bands (2–4 GHz): balance.  
High bands (>24 GHz mmWave): hotspots.  
12.2.3 Backhaul  
Microwave links (cost-effective in ASEAN rural).  
Fiber (preferred in cities like Sydney, Singapore).  
Satellite integration for remote ANZ deployments.

Page 83

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 84 of 114  12.3 Optimization Techniques  
12.3.1 Radio Optimization  
Antenna tilt and azimuth adjustments.  
Power control:  Reducing interference, extending battery life.  
Carrier aggregation balancing.  
12.3.2 Interference Management  
ICIC/eICIC (Inter-Cell Interference Coordination).  
Coordinated multipoint (CoMP).  
Beamforming calibration in 5G.  
12.3.3 Load Balancing  
Cell reselection parameters tuned dynamically.  
Traffic steering  between LTE, 5G, and Wi-Fi.  
12.4 Testing Methodologies  
12.4.1 Drive Testing  
 Traditional: engineers drive routes collecting RSRP, SINR, throughput.  
 Tools: TEMS, Nemo, Rohde & Schwarz scanners.  
 Challenge: costly, limited coverage.  
12.4.2 Crowdsourced Testing  
 App-based KPI collection (Ookla, OpenSignal).  
 Offers broader coverage, lower cost.  
 Privacy and accuracy remain issues.  
12.4.3 Lab Testing  
 Pre-deployment functional tests (conformance, stress, regression).  
 Simulated RAN and core environments.

Page 84

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 85 of 114  12.4.4 Field Trials  
 Phased rollout in test markets.  
 Regulatory and safety approval required.  
12.5 AI and Data Science in Optimization  
12.5.1 Automated Parameter Tuning  
 AI systems self-adjust handover thresholds, antenna tilt, and power levels.  
 Reinforcement learning optimizes beamforming in dense urban ASEAN 
networks.  
12.5.2 Predictive Network Planning  
 ML models forecast demand (e.g., holiday surges in Manila).  
 Clustering algorithms identify underperforming cells.  
12.5.3 Fault Detection  
 AI detects anomalies in KPI streams.  
 Self-healing RAN reroutes traffic automatically.  
12.5.4 Testing Automation  
 Digital twins simulate network rollout before deployment.  
 AI accelerates regression testing of core functions.

Page 85

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 86 of 114  12.6 ASEAN and ANZ Deployment Context  
12.6.1 ASEAN  
 Singapore: strong reliance on indoor small cells  and HetNets.  
 Indonesia: challenge of archipelagic geography  → satellite backhaul + 
microwave.  
 Vietnam/Thailand: government-driven 5G pilots with state-owned carriers.  
12.6.2 ANZ  
 Australia: Telstra and Optus focus on nationwide coverage, hybrid 
satellite integration.  
 New Zealand: Spark targets agriculture IoT  with precision deployment of 
LoRaWAN and LTE-M.  
12.7 Comparative Testing Approaches  
Method Cost Coverage  Accuracy  AI Integration  
Drive Test  High Limited High Low 
Crowdsourced  Low Wide Moderate  High (data science pipelines)  
Lab Testing  Medium Simulated  High High 
Field Trial  Medium Targeted  High Medium 
 
12.8 Challenges in Optimization  
 Heterogeneous networks (HetNets):  Complicated interference patterns.  
 Multi-vendor environments:  Interoperability issues.  
 Cost constraints:  Especially in emerging ASEAN markets.  
 AI trustworthiness:  Models must be explainable, not black-box.

Page 86

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 87 of 114  12.9 Case Studies  
12.9.1 Singapore 5G Standalone  
 Heavy reliance on AI-automated optimization.  
 Digital twin used for Marina Bay 5G rollout.  
12.9.2 Australia Remote Connectivity  
 AI-driven planning of satellite-cellular hybrid networks.  
 Starlink integrated into outback schools with predictive traffic balancing.  
12.10 Summary  
Deployment and optimization evolved from manual field-driven engineering  to AI-
powered, data-driven automation.  
 2G/3G: manual drive testing and parameter tuning.  
 4G: semi-automated SONs.  
 5G: digital twins, AI-optimized RAN, predictive deployment.  
In ASEAN, urban density and geographic fragmentation  dominate deployment 
challenges. In ANZ, vast rural areas and industrial IoT  drive reliance on automation 
and satellite integration. 
END OF CHAPTER 12

Page 87

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 88 of 114   
Chapter 13 – Case Studies and Comparative Analysis  
 
13.1 Introduction  
Case studies illustrate how different mobile and wireless technologies have been 
applied in real-world contexts . Comparative analysis highlights strengths, 
weaknesses, and regional suitability  across ASEAN and ANZ.  
This chapter reviews use cases in cellular generations (2G–5G), Wi-Fi, LPWAN 
(LoRaWAN), mission-critical systems (TETRA ATEX, ST GRID), and satellite 
networks  (Beidou, Starlink, Inmarsat, etc.).  
 
13.2 Case Study 1 – GSM/2G in ASEAN (1990s–2000s)  
Deployment  
 GSM widely deployed in ASEAN from 1993 onward.  
 900/1800 MHz bands.  
 Enabled SMS culture  in Southeast Asia.  
Impact 
 First affordable digital voice in rural and urban areas.  
 Led to economic transformation (mobile banking in Philippines).  
Challenges  
 Weak encryption (A5/1 cracked).  
 Legacy persistence: 2G still active in parts of Myanmar, Cambodia.

Page 88

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 89 of 114  13.3 Case Study 2 – UMTS/3G in ANZ (2005–2015)  
Deployment  
 Telstra’s Next G network (850 MHz)  in Australia was world-leading.  
 Extended 3G coverage across rural Australia.  
Impact 
 Internet access in previously isolated communities.  
 Enabled agricultural IoT pilot projects.  
Challenges  
 High CAPEX for nationwide deployment.  
 3G sunset (2024–2025) creates migration challenges.  
13.4 Case Study 3 – 4G LTE in ASEAN Megacities  
Deployment  
 Dense rollouts in Bangkok, Jakarta, Manila.  
 Spectrum used: 1800 MHz, 2100 MHz, 2600 MHz.  
Impact 
 Mobile broadband penetration surged (>80% smartphone adoption).  
 Gig economy (Grab, Gojek) flourished with LTE connectivity.  
Challenges  
 Congestion in dense urban environments.  
 Interference management critical.

Page 89

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 90 of 114  13.5 Case Study 4 – 5G in Singapore and Australia  
Deployment  
 Singapore: 5G standalone core with slicing (2020+).  
 Australia: Telstra and Optus launched 5G FWA (Fixed Wireless Access).  
Impact 
 Singapore: 5G-enabled autonomous vehicles, smart ports.  
 Australia: rural households gain high-speed internet via 5G FWA + Starlink.  
Challenges  
 ASEAN: patchy adoption, limited spectrum in some markets.  
 ANZ: mmWave rollout limited by cost and coverage.  
13.6 Case Study 5 – Wi-Fi 6 in ASEAN Enterprises  
Deployment  
 Wi-Fi 6 deployed in airports, universities, business parks.  
 Complementary to 5G indoor deployments.  
Impact 
 Supports enterprise digitalization.  
 Singapore: hybrid Wi-Fi/5G private networks in Changi Airport.  
Challenges  
 Congestion in 2.4 GHz band.  
 Limited indoor spectrum coordination with cellular.

Page 90

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 91 of 114  13.7 Case Study 6 – LoRaWAN in New Zealand Agriculture  
Deployment  
 LoRaWAN deployed for sheep/livestock monitoring, irrigation.  
 Coverage: 10–15 km from base stations.  
Impact 
 Improved productivity and water efficiency.  
 Reduced OPEX due to battery life (10 years+).  
Challenges  
 Low data rates (not suitable for multimedia telemetry).  
 Dependence on unlicensed spectrum (interference risk).

Page 91

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 92 of 114  13.8 Case Study 7 – TETRA ATEX in ASEAN Oil & Gas  
Deployment  
 ATEX-certified TETRA radios used on offshore rigs in Brunei, Malaysia.  
 Mission-critical communication in hazardous zones.  
Impact 
 Safety compliance in flammable environments.  
 Robust voice communications under extreme conditions.  
Challenges  
 Not suitable for data-heavy IoT.  
 Proprietary devices with high cost.  
13.9 Case Study 8 – Starlink in Rural ANZ  
Deployment  
 Starlink terminals installed in Australian Outback schools and New Zealand 
farms. 
 Latency ~30–40 ms, throughput 50–200 Mbps.  
Impact 
 Bridged digital divide in rural education.  
 Enabled telehealth and online education.  
Challenges  
 Cost of terminals (~USD 600) limits adoption.  
 Weather sensitivity (rain fade).

Page 92

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 93 of 114  11.10 Comparative Technology Table  
Technology  Latency Throughput  Strengths  Limitations  ASEAN/ANZ Use Case  
GSM/EDGE  150+ ms  ~0.2 Mbps  Coverage, 
maturity Low data  SMS-driven culture in 
ASEAN 
UMTS/HSPA  50–100 
ms ~14–42 Mbps  Proven, flexible  Interference  Telstra Next G rural 
coverage  
LTE 20–50 ms  100s Mbps–
Gbps Ecosystem, 
efficiency  Dense 
planning  ASEAN megacities 
broadband  
5G NR <10 ms >1 Gbps Low latency, 
slicing Complexity  Singapore smart nation  
Wi-Fi 6 <10 ms Multi-Gbps  Cost-effective  Interference  Airports, enterprises  
LoRaWAN  Seconds kbps Long range, IoT  Low 
throughput  NZ agriculture  
TETRA 
ATEX ~100 ms  Narrowband  Mission-critical  Not 
broadband  Oil & gas ASEAN rigs  
Starlink 30–40 ms  50–200 Mbps  Rural coverage  Cost, weather  ANZ schools & farms  
 
13.11 AI/ML in Case Study Applications  
 ASEAN megacities (LTE/5G):  AI-driven interference coordination.  
 ANZ rural Starlink:  AI optimizes satellite beam allocation.  
 LoRaWAN NZ farms:  ML models predict soil irrigation schedules.  
 Singapore 5G slicing:  Reinforcement learning allocates slices dynamically.

Page 93

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 94 of 114  13.12 Summary  
Case studies demonstrate the complementary role  of mobile, Wi-Fi, LPWAN, 
mission-critical, and satellite systems:  
 2G/3G laid foundations but persist as vulnerabilities.  
 4G/5G dominate urban ASEAN/ANZ broadband.  
 Wi-Fi 6 complements indoor capacity.  
 LoRaWAN  thrives in agriculture IoT.  
 TETRA ATEX  ensures mission-critical safety.  
 Starlink bridges rural broadband gaps.  
AI/ML consistently enhances performance, reliability, and security across all case 
studies. 
END OF CHAPTER 13

Page 94

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 95 of 114  Chapter 15 – 5G Advanced and the Road to 6G  
 
15.1 Introduction  
While 5G networks  are still being deployed globally, research and standardization 
have already shifted toward 5G Advanced (3GPP Release 18 and beyond)  and 6G 
(expected 2030).  
The focus is on making networks:  
 AI-native  (intelligence embedded into the protocol stack).  
 Sustainable  (green networking, energy efficiency).  
 Ubiquitous  (integrated terrestrial, aerial, and satellite).  
 Convergent  (communications, sensing, and computing combined).  
 
15.2 5G Advanced (Release 18–20)  
15.2.1 Key Enhancements  
 Extended MIMO:  Extremely large antenna arrays with AI beam 
management.  
 Positioning Accuracy:  Sub-meter precision for industrial IoT and 
autonomous vehicles.  
 XR Support:  Optimized for AR/VR with latency <5 ms.  
 Uplink-Centric Design:  Prioritizing sensor-heavy IoT and metaverse 
devices. 
 Non-Terrestrial Networks (NTN):  Native satellite integration (LEO, 
GEO, HAPS).  
15.2.2 AI-Native RAN  
 AI models trained to optimize scheduling, resource allocation, and 
interference coordination.  
 Network functions exposed as APIs for real-time ML training.

Page 95

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 96 of 114  15.2.3 ASEAN/ANZ Applications  
 ASEAN: Smart factories in Malaysia, Thailand → low-latency positioning.  
 ANZ: Agriculture robotics with uplink-heavy IoT, powered by AI-
optimized 5G.  
 
15.3 Toward 6G (IMT-2030)  
15.3.1 Target Capabilities  
 Data Rates:  Up to 1 Tbps peak.  
 Latency:  Sub-millisecond (0.1–0.5 ms).  
 Sensing: Integrated radar-like capabilities in radio beams.  
 AI-Native Architecture:  AI/ML embedded at protocol design, not as an 
add-on. 
 Sustainability:  Net-zero carbon networks.  
15.3.2 Frequency Ranges  
 Sub-THz (100–300 GHz):  Provides extreme bandwidth but limited range.  
 Visible Light Communication (VLC):  Integration with optical systems.  
15.3.3 Key Enablers  
 Reconfigurable Intelligent Surfaces (RIS):  Smart reflective panels 
steering signals dynamically.  
 Extreme MIMO:  Thousands of antenna elements.  
 Joint Communication and Sensing (JCAS):  Networks double as 
radar/sensors.  
 Quantum-Safe Security:  Resistant to post-quantum attacks.

Page 96

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 97 of 114  15.4 AI and Data Science in 6G  
 AI-Centric Design:  Scheduling, waveform selection, and topology self-
optimized.  
 Digital Twins:  Entire networks simulated in real-time for predictive 
optimization.  
 Federated Learning:  Privacy-preserving AI training across devices.  
 Self-Evolving Networks:  Reinforcement learning adapts protocols on the fly.  
 
15.5 Non-Terrestrial Networks (NTN)  
15.5.1 Satellite Integration  
 5G Advanced → 6G:  Direct-to-device satellite service.  
 LEO constellations integrated with cellular core networks.  
15.5.2 ASEAN/ANZ Relevance  
 ASEAN: Disaster recovery networks after typhoons/earthquakes.  
 ANZ: Outback IoT (mining, agriculture) seamlessly using NTN.  
 
15.6 6G Use Cases  
15.6.1 Industrial & Autonomous Systems  
 ASEAN: Autonomous ports in Singapore, Malaysia.  
 ANZ: Robotic farming with AI-powered real-time sensing.  
15.6.2 Extended Reality (XR) & Metaverse  
 Holographic communication with Tbps uplink/downlink.  
 Seamless global presence for work and entertainment.  
15.6.3 Smart Cities & Environmental Monitoring  
 ASEAN megacities:  Real-time air pollution sensing + traffic management.  
 ANZ: Wildfire detection via UAV-6G integrated systems.

Page 97

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 98 of 114  15.7 Comparative Timeline  
Generation  Peak Rate  Latency Key Innovations  
4G LTE  1 Gbps 20–50 ms  All-IP, MIMO  
5G NR 10 Gbps <1 ms Slicing, massive MIMO, NTN  
5G Advanced  ~20 Gbps  <0.5 ms AI-native RAN, XR, precise positioning  
6G 1 Tbps 0.1–0.5 ms  RIS, JCAS, sub-THz, AI-centric  
 
15.8 Challenges  
 Hardware Constraints:  Sub-THz requires new semiconductor materials.  
 Energy Consumption:  Ultra-dense antennas must be energy-efficient.  
 Standardization:  Global consensus on IMT-2030 framework.  
 Security:  Quantum-resistant cryptography needed.  
 
15.9 ASEAN and ANZ Roadmaps  
15.9.1 ASEAN  
 Singapore, Thailand, Malaysia actively participating in ITU 6G studies.  
 Focus on smart cities, industrial IoT.  
15.9.2 ANZ  
 Australia and New Zealand prioritizing agriculture, mining, and remote 
connectivity.  
 Early adoption of NTN for vast territories.

Page 98

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 99 of 114  15.10 Summary  
5G Advanced  strengthens current 5G with AI-native features, uplink 
optimization, and satellite integration. 
 
6G envisions AI as the network itself : Tbps speeds, near-zero latency, 
sensing + communications convergence.  
ASEAN and ANZ will apply these differently:  
 ASEAN → dense urban smart cities, Industry 4.0.  
 ANZ → agriculture, mining, remote IoT with satellite integration.  
END OF CHAPTER 15

Page 99

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 100 of 114   
Chapter 17 – Conclusion  
 
17.1 Overview  
This thesis traced the technical evolution of mobile and wireless systems  from the 
analog 1G era of the 1980s to 5G and the trajectory toward 6G , while also exploring 
parallel ecosystems  such as Wi-Fi, LoRaWAN, TETRA ATEX, ST GRID devices, 
and satellite networks (Beidou, Starlink, Inmarsat, Gouwang, Qianfan).  
Across ASEAN and ANZ, the comparative analysis demonstrated that technology 
adoption reflects regional socio-economic and geographic realities : 
 ASEAN megacities  → dense LTE/5G rollouts, smart city integration.  
 ANZ rural/remote regions  → satellite and hybrid systems critical for 
inclusion.  
 
17.2 Key Findings  
17.2.1 Generational Progression  
 1G: Introduced cellular concept, but no security.  
 2G: Digitalization, SMS culture, basic security.  
 3G: Mobile broadband foundation, yet uneven global adoption.  
 4G LTE:  All-IP, high capacity, foundation for the gig economy.  
 5G NR: AI-ready, ultra-low latency, slicing, satellite integration.  
17.2.2 Specialized Systems  
 LoRaWAN:  Ideal for long-range IoT, but limited throughput.  
 TETRA ATEX / ST GRID:  Mission-critical, ruggedized comms for safety 
industries.  
 Satellite Systems:  Complement terrestrial, crucial for rural ANZ and disaster-
stricken ASEAN.

Page 100

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 101 of 114  17.2.3 AI and Data Science  
Now central to:  
 Traffic prediction.  
 Security anomaly detection.  
 Automated optimization (SON, digital twins).  
 QoS and QoE assurance.  
17.3 Regional Insights  
ASEAN 
 Diversity in deployment maturity: Singapore (5G advanced), 
Cambodia/Myanmar (legacy 2G still active).  
 Smart city initiatives driving IoT adoption.  
 Geographical fragmentation (archipelagos like Indonesia, Philippines) → 
satellite + terrestrial hybrids.  
ANZ 
 Vast landmass and low population density → heavy reliance on macrocells + 
satellite. 
 Strong integration of LoRaWAN and LTE-M  in agriculture.  
 Starlink + Telstra partnerships extend digital equity in Australia’s Outback.

Page 101

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 102 of 114  17.4 Future Outlook  
17.4.1 5G Advanced  
 Strengthens positioning, uplink, XR, and NTN.  
 AI becomes intrinsic to RAN/core functions.  
17.4.2 6G Vision  
 1 Tbps peak rates, 0.1 ms latency.  
 Integrated communication + sensing.  
 Reconfigurable Intelligent Surfaces (RIS), sub-THz spectrum.  
 Quantum-safe, AI-native networks.  
ASEAN will leverage 6G for urban industrial automation , while ANZ will apply it 
to agriculture, mining, and remote connectivity.  
 
17.5 Research Methodology Reflection  
 Historical review:  Standards evolution analyzed from 3GPP, ITU, IEEE 
documentation.  
 Comparative analysis:  Technologies benchmarked in terms of latency, 
throughput, security, and use cases.  
 Case studies:  ASEAN/ANZ deployments studied from operator whitepapers, 
government policies, and academic research.  
 AI/ML integration:  Evaluated through contemporary research on SONs, 
digital twins, and anomaly detection.

Page 102

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 103 of 114  17.6 Final Thoughts  
The central theme is convergence : 
 Cellular, Wi-Fi, LPWAN, and satellite are not competing  but complementary.  
 AI/ML ensures networks become adaptive, autonomous, and predictive.  
 Future 6G will be a cognitive fabric  — sensing, computing, and 
communicating seamlessly.  
The evolution from 1G to 5G and beyond  is not merely technological but also socio-
economic , shaping digital inclusion in ASEAN and ANZ. The challenge for researchers 
and policymakers is ensuring that technological sophistication aligns with 
accessibility, affordability, and security.

Page 103

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 104 of 114  REFERENCES & BIBIOGRAPHY  
 
[1] 
Canonical: 3GPP TS 23.501, "System Architecture for the 5G System (5GS)" 
URL: https://www.3gpp.org/ftp/Specs/archive/23_series/23.501/ 
Used In Thesis Section(s): Ch.8 Core Networks (MSC→EPC→5GC), Ch.6 Standards 
(5G), Ch.13 5G-Advanced 
Referenced Statement: 5GC is a cloud-native Service-Based Architecture (SBA) 
with functions such as AMF, SMF, UPF, NRF, PCF communicating via service 
APIs. 
 
[2] 
Canonical: 3GPP TS 38.300, "NR; NR and NG‑RAN overall description" 
URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.300/ 
Used In Thesis Section(s): Ch.6 Standards (5G NR), Ch.8 Core Networks (RAN ↔ 
Core interfaces) 
Referenced Statement: Overall description of 5G NR/NG‑RAN, including 
architecture, protocol layers, and reference signals. 
 
[3] 
Canonical: 3GPP TS 36.300, "E‑UTRA and E‑UTRAN; Overall description; Stage 2" 
URL: https://www.3gpp.org/ftp/Specs/archive/36_series/36.300/ 
Used In Thesis Section(s): Ch.5 Topologies (macro/micro/pico), Ch.6 Standards 
(LTE), Ch.4 Modulation/Access 
Referenced Statement: LTE uses OFDMA (downlink) and SC‑FDMA 
(uplink) and defines key E‑UTRAN procedures . 
 
[4] 
Canonical: ETSI GSM 05.05 (3GPP TS 45.005), "Radio transmission and reception" 
URL: https://www.etsi.org/deliver/etsi_gts/05/0505/05.05.01_60/  |  
https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?speci
ficationId=1448 
Used In Thesis Section(s): Ch.2 Timeline (2G), Ch.6 Standards (GSM) 
Referenced Statement: GSM carrier spacing is 200 kHz; baseline RF 
characteristics for GSM. 
 
[5] 
Canonical: 3GPP HSPA (Release 5/6) — see TS 25.308 / TS 25.306; summary: "High 
Speed Packet Access (HSDPA/HSUPA)" 
URL: 
https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?speci
ficationId=1411 
Used In Thesis Section(s): Ch.2 Timeline (3G), Ch.6 Standards (UMTS/HSPA) 
Referenced Statement: HSDPA introduced downlink peak rates on the order of 
~14.4 Mb/s with link adaptation and fast scheduling .

Page 104

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 105 of 114  [6] 
Canonical: 3GPP TR 38.901, "Study on channel model for frequencies from 0.5 to 
100 GHz" 
URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.901/ 
Used In Thesis Section(s): Ch.3 Propagation & Link Budget, Ch.6 Standards (5G 
NR), Ch.13 5G‑Advanced 
Referenced Statement: NR channel modeling across sub‑6 GHz and mmWave, 
including path loss, delay spread, and mobility (Doppler) effects. 
 
[7] 
Canonical: ITU‑R P.525‑4, "Calculation of free-space attenuation" 
URL: https://www.itu.int/rec/R-REC-P.525/en 
Used In Thesis Section(s): Ch.3 Propagation & Link Budget 
Referenced Statement: Free‑space path loss (FSPL) formula used as baseline for 
link budgets. 
 
[8] 
Canonical: 3GPP TS 33.501, "Security architecture and procedures for 5G System" 
URL: https://www.3gpp.org/ftp/Specs/archive/33_series/33.501/ 
Used In Thesis Section(s): Ch.9 Security & Authentication, Ch.6 Standards (5G), 
Ch.8 Core Networks 
Referenced Statement: 5G‑AKA, SUCI (concealed SUPI), and mapping of 
integrity/confidentiality algorithms (e.g., NEA1/2/3, NIA1/2/3). 
 
[9] 
Canonical: NIST Cybersecurity White Paper — SUCI Overview (supporting 5G 
privacy) 
URL: https://csrc.nist.gov/publications 
Used In Thesis Section(s): Ch.9 Security & Authentication 
Referenced Statement: Rationale and mechanism for protecting subscriber 
identifiers via SUCI to mitigate IMSI‑catcher attacks . 
 
[10] 
Canonical: LoRa Alliance, "LoRaWAN® L2 1.0.4 Specification" 
URL: https://lora-alliance.org/resource_hub/lorawan-specification-1-0-4/  |  
https://resources.lora-alliance.org/ 
Used In Thesis Section(s): Ch.7 Specialized Systems (LoRaWAN), Ch.6 Standards 
(LPWAN) 
Referenced Statement: Device classes (A/B/C), MAC behavior, and certification 
references for LPWAN deployments.

Page 105

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 106 of 114  [11] 
Canonical: IEEE 802.11ax (Wi‑Fi 6) standard — overview via vendor technical white 
papers 
URL: https://www.cisco.com/c/en/us/solutions/collateral/enterprise-networks/802-
11ax-solution/white-paper-c11-740788.html 
Used In Thesis Section(s): Ch.6 Standards (Wi‑Fi), Ch.10 Performance/QoS (dense 
deployments) 
Referenced Statement: OFDMA, BSS Coloring, and Target Wake Time (TWT) as 
key Wi‑Fi 6 features for efficiency in dense environments. 
 
[12] 
Canonical: LANCOM Systems Whitepaper (Wi‑Fi 6) 
URL: https://www.lancom-systems.com/wi-fi6-whitepaper/ 
Used In Thesis Section(s): Ch.6 Standards (Wi‑Fi) 
Referenced Statement: Practical explanations of 802.11ax mechanisms (OFDMA, 
BSS Coloring) and deployment guidance.  
 
[13] 
Canonical: IEEE 802.16e‑2005 (Mobile WiMAX) — mobility/QoS extensions 
URL: https://standards.ieee.org/standard/802_16e-2005.html  |  
https://ws680.nist.gov/publication/get_pdf.cfm?pub_id=150568 
Used In Thesis Section(s): Ch.6 Standards (WiMAX), Ch.2 Timeline (4G contenders) 
Referenced Statement: Mobile WiMAX extensions enabling mobility, QoS, and 
PHY/MAC enhancements; historical 4G context.  
 
[14] 
Canonical: 3GPP TR 38.821, "Solutions for NR to support non‑terrestrial networks 
(NTN)" 
URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.821/ 
Used In Thesis Section(s): Ch.6 Standards (5G/NTN), Ch.7 Specialized Systems 
(Satellites), Ch.13 Road to 6G 
Referenced Statement: Architectural and PHY/MAC adaptations for NR over 
LEO/HAPS, including timing/Doppler considerations. 
 
[15] 
Canonical: 3GPP TS 38.211/38.213/38.214 (NR PHY, control, and procedures) — 
numerology 
URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.211/ 
Used In Thesis Section(s): Ch.4 Modulation/Coding/Multiple Access, Ch.6 Standards 
(5G NR) 
Referenced Statement: NR subcarrier spacing Δf = 15×2^μ kHz, slot/frame 
structure, MCS tables and link adaptation context.

Page 106

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 107 of 114  [16] 
Canonical: ETSI/3GPP Algorithm specifications (Snow3G, AES‑CTR, ZUC) — 
mapped via TS 33.501 Annexes 
URL: https://www.3gpp.org/ftp/Specs/archive/35_series/  |  
https://www.3gpp.org/ftp/Specs/archive/33_series/33.501/ 
Used In Thesis Section(s): Ch.9 Security & Authentication 
Referenced Statement: Families of confidentiality (NEA1/2/3) and integrity 
(NIA1/2/3) algorithms used in LTE/NR. 
 
[17] 
Canonical: ITU‑R P.838‑3 (specific attenuation model for rain) and P.840‑8 (rain 
height/cloud models) 
URL: https://www.itu.int/rec/R-REC-P.838/en  |  https://www.itu.int/rec/R-REC-
P.840/en 
Used In Thesis Section(s): Ch.3 Propagation (mmWave rain fade), Ch.13 
5G‑Advanced (FR2 planning) 
Referenced Statement: Quantitative rain attenuation models applicable to 
FR2/mmWave planning. 
 
[18] 
Canonical: RF Cafe (historical engineering tables) — AMPS 30 kHz channelization 
URL: https://www.rfcafe.com/references/electrical/wireless-communications-
standards.htm 
Used In Thesis Section(s): Ch.2 Timeline (1G), Ch.6 Standards (1G) 
Referenced Statement: AMPS channel spacing of 30 kHz and associated band 
plans (historical reference). 
 
[19] 
Canonical: 3GPP TS 23.401 (EPC); IMS/VoLTE (TS 24.229); 24/26 series for 
SIP/codec 
URL: https://www.3gpp.org/ftp/Specs/archive/23_series/23.401/  |  
https://www.3gpp.org/ftp/Specs/archive/24_series/24.229/  |  
https://www.3gpp.org/ftp/Specs/archive/26_series/ 
Used In Thesis Section(s): Ch.8 Core Networks (LTE/EPC), Ch.10 Performance/QoS 
Referenced Statement: EPC split (MME, SGW, PGW) and IMS/VoLTE SIP-call 
control with codec implications. 
 
[20] 
Canonical: 3GPP TS 36.212/36.213 (LTE PHY coding & modulation; link 
adaptation) 
URL: https://www.3gpp.org/ftp/Specs/archive/36_series/ 
Used In Thesis Section(s): Ch.4 Modulation/Coding (4G), Ch.10 Performance/QoS 
Referenced Statement: Coding/modulation schemes, HARQ, higher-order QAM, 
and MCS tables used for LTE/LTE‑A throughput claims.

Page 107

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 108 of 114  [21] 
Canonical: 3GPP TS 45.002/45.003 (GSM physical channel, modulation) 
URL: https://www.3gpp.org/ftp/Specs/archive/45_series/ 
Used In Thesis Section(s): Ch.4 Modulation/Coding (2G), Ch.2 Timeline (2G) 
Referenced Statement: GMSK modulation, time-slot/multiframe structure for 
GSM; groundwork for GPRS/EDGE evolution. 
 
[22] 
Canonical: 3GPP TS 25.331/25.211 (UMTS/W‑CDMA RRC & PHY) 
URL: https://www.3gpp.org/ftp/Specs/archive/25_series/ 
Used In Thesis Section(s): Ch.4 Modulation/Coding (3G), Ch.2 Timeline (3G) 
Referenced Statement: W‑CDMA spreading/channelization and RRC procedures; 
HSPA context. 
 
[23] 
Canonical: IEEE 802.11be (Wi‑Fi 7) – overview (IEEE/vendor) 
URL: https://standards.ieee.org/ieee/802.11be/7421/  |  
https://www.intel.com/content/www/us/en/products/docs/wireless/wi-fi-7.html 
Used In Thesis Section(s): Ch.6 Standards (Wi‑Fi evolution), Ch.10 Performance/QoS 
Referenced Statement: 320‑MHz channels and Multi‑Link Operation (MLO) as 
Wi‑Fi 7 advancements. 
 
[24] 
Canonical: LoRa Alliance — Regional Parameters (RP2‑1.0.4) 
URL: https://lora-alliance.org/resource_hub/rp2-1-0-4-lorawan-regional-parameters/ 
Used In Thesis Section(s): Ch.7 Specialized Systems (LoRaWAN), Ch.3 Spectrum 
(ISM bands) 
Referenced Statement: Regional channel plans, duty‑cycle, and EIRP constraints 
for LoRaWAN. 
 
[25] 
Canonical: ITU IMT documents (IMT‑Advanced & IMT‑2020 
requirements/definitions) 
URL: https://www.itu.int/en/ITU-R/terrestrial/im/pages/im.aspx 
Used In Thesis Section(s): Ch.2 Timeline (4G/5G definitions), Ch.6 Standards 
Referenced Statement: Formal generational requirements and performance 
frameworks for IMT‑Advanced (4G) and IMT‑2020 (5G).

Page 108

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 109 of 114  Image Credits  
Credits to image owners under Creative Commons or taken in full context  
 
 
 
 
Science Museum Group. Motorola StarTAC mobile phone, 1997.. 1997-1650 Science 
Museum Group Collection Online. Accessed 4 October 2025. 
https://collection.sciencemuseumgroup.org.uk/objects/co430373/motorola-startac-mobile-
phone-1997 . 
 
 
 
 
Science Museum Group. Motorola 4800 Analogue Mobile. Y2005.44.1 
Science Museum Group Collection Online. Accessed 5 October 2025. 
https://collection.sciencemuseumgroup.org.uk/objects/co8413432/motorola-4800-analogue-
mobile. 
 
Science Museum Group. Motorola A1000 mobile 
phone. 2014-495/1 Science Museum Group 
Collection Online. Accessed 5 October 2025. 
https://collection.sciencemuseumgroup.org.uk/objects/co8442766/motorola-a1000-mobile-
phone

Page 109

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 110 of 114   
 
Science Museum Group. Nokia 9000 Communicator 
mobile phone. 2014-44 Science Museum Group 
Collection Online. Accessed 5 October 2025. 
https://collection.sciencemuseumgroup.org.uk/objects/co8439032/nokia-9000-communicator-
mobile-phone  
Credit to SAMSUNG (Galaxy A12, taken out 
from a free-to-distribute) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Apple IPhone17 Pro Max © APPLE.COM

Page 110

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 111 of 114  POST GRADUATE PATHWAY

Page 111

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 112 of 114  KEY TAKE AWAY IMAGES   
 
Abstract & Intro  
 Timeline overview (1980s–5G).  
 Conceptual diagram: radio systems ecosystem (cellular, Wi-Fi, LPWAN, 
satellite).  
 World map showing ASEAN & ANZ focus.  
Chapter 1 – Introduction  
 Cellular architecture basics.  
 Comparative chart: wireless vs wired.  
 Illustration of spectrum evolution.  
Chapter 2 – Historical Timeline  
 1G–5G evolution timeline.  
 Generational throughput/latency chart.  
 Device evolution pictorial (brick phone → smartphone → IoT).  
Chapter 3 – Spectrum & Regulation  
 900 MHz propagation graph.  
 ASEAN spectrum allocation table (visualized).  
 ANZ spectrum licensing diagram.  
Chapter 4 – Propagation & Link Budget  
 Path loss models (free space vs Hata).  
 Link budget block diagram.  
 Antenna/MIMO illustration.  
Chapter 5 – Network Topologies  
 Macro/micro/pico/femto cell hierarchy.  
 HetNet diagram.

Page 112

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 113 of 114   Mesh/LPWAN star-of-stars.  
Chapter 6 – Standards by Generation  
 Table: 1G–5G standards comparison.  
 Wi-Fi evolution chart.  
 LoRaWAN architecture.  
Chapter 7 – Specialized Systems  
 TETRA ATEX rugged radio pictorial.  
 ST GRID hybrid phone diagram.  
 Satellite constellations map (Beidou, Starlink, Inmarsat).  
Chapter 8 – Core Networks  
 MSC → EPC → 5GC evolution.  
 Service-based architecture diagram.  
 NFV/SDN cloud-native illustration.  
Chapter 9 – Security & Authentication  
 Authentication methods evolution (1G–5G).  
 Attack surface diagram (IMSI catcher, rogue base station).  
 AI-based intrusion detection pipeline.  
Chapter 10 – Performance & QoS  
 KPI dashboard mockup.  
 QCI/5QI mapping table.  
 Throughput/latency scatterplot.  
Chapter 11 – Deployment & Testing  
 Drive test vs crowdsourced testing diagram.  
 Digital twin illustration.  
 Optimization workflow (AI loop).

Page 113

 
 © AZRIN.INFO 2025 
UNIVERSITA’ DEGLI GUGLIELMO MARCONI                                      Page 114 of 114  Chapter 12 – Case Studies  
 ASEAN adoption map.  
 ANZ rural broadband coverage.  
 Comparative chart: LTE vs Starlink vs LoRaWAN.  
Chapter 13 – 5G Advanced & 6G  
 RIS (reconfigurable intelligent surfaces) pictorial.  
 Sub-THz spectrum chart.  
 6G vision roadmap (timeline).  
Chapter 14 – Conclusion  
 Pyramid diagram (1G foundation → 6G cognitive fabric).  
 ASEAN vs ANZ summary chart.  
 Future integration flowchart (AI + satellite + terrestrial).

Page 114

Thesis Paper - Mobile& Wireless Networks – Evolution from 1980s to 5G and Beyond.

2025-10-14T00:30:00.000-07:00

UNIVERSITA’ DEGLI STUDI GUGLIELMO MARCONI

Master of Science in Data Science & Artificial Intelligence (AI)

Mobile& Wireless Networks – Evolution from 1980s to 5G and Beyond.

Academic Advisor Candidate

DHANABAL T AZRIN ABDUL MAJID

Lithan Academy Rapheal Azrin

MCSLT00084

UNIVERSITA’ DEGLI STUDI GUGLIELMO MARCONI

ACADEMIC YEAR 2025

Table of Contents

Section	Page
Chapter 1 Introduction & Research Methodology	1
· Research & Discussion on Possible Topics	5
· Brainstorming	7
Chapter 2 - Literature Review	9
· Thesis Preface & Introduction	11
· Candidate Background	12
Chapter 3 - Thesis Abstract	13
· Thesis Statement	14
· Dedication and Thanks	15
Chapter 4 - Historical Timeline (1G to 5G and early 6G research)	22
Chapter 5 - Spectrum & Regulation (ASEAN/ANZ focus, 900 MHz, mmWave, etc.)	32
· Annex - AI & ML for Propagator Prediction	38
Chapter 6 - Modulation, Coding, Multiple Access	39
Chapter 7 - Network Topologies & Hierarchies	44
Chapter 8 - Standards by Generation (1G–5G, WiFi, WiMAX, LoRaWAN)	51
Chapter 9- Specialized Systems & Use Cases (LoRaWAN, TETRA ATEX, ST GRID, Satellites: Beidou, Starlink, Inmarsat)	58
Chapter 10 - Core Networks (MSC to 5GC with AI/ML enhancements)	65
Chapter 11 - Security & Authentication	71
Chapter 12 - Performance, QoS, KPI Engineering (with Data Science)	76
Chapter 13 - Deployment, Optimization, Testing (AI-driven SON, ML analytics)	81
Chapter 14 - Comparative Case Studies (ASEAN & ANZ)	86
Chapter 15 - 5G Advanced & Road to 6G	91
Chapter 16 - Conclusion	96
Chapter 17 - References & Bibliography	101

Chapter 1 Introduction

This paper is to demonstrate the competence level of the Postgraduate Masters Degree by Coursework candidate, Mr Azrin Bin Abdul Majid aka RAPHEAL AZRIN, to the competency level for the course on Data Science and Artificial Intelligence, in the academic year of 2025.

The task assigned by the teaching staff is to gather information, determine a sub-deviation of the topics being tabled, and carefully expand and research into the subject matters, in a miniature brief of 12,000 worded paper , with graphics et al.

A Long list of topics and their Research Methodology were proposed.

Added to the ask is that, the usage of AI ,DATASCIENCE, Cybersecurity et al, as the candidate is attempting a Masters of Science in Data Science and AI under Lithan-UniMarconi collaboration and executive Masters program as part of the Skill future Career Transition Program, with 90% Funded by the Singapore Government vis Skillfuture Credits + CoPayment by the student on the PGDip segment.

The Objective of the program is to ensure the student can demonstrate the ability to apply and use case of the knowledge and skills learnt thru the SCTP Module , Data Science Modelling, Deep Learning, AML & Methods, Data Science Principles, Python for DS, DMV Data Modelling & Visualization, Generative AI & Project Management.

Deliberations of the modelling and reasoning brings us to the below mentioned. Following are the proposed topics tabled by the senate as appropriate for the cohort for Data Science, Security Management and Cloud Administration.

Generative and Agentic AI Agent services were used in the research to expedite results and research, cross referencing against the generative bias of agents such as ChatGPT5, CoPilot365, Gemini Enterprise and DeepSeek alongside other agentic workflow systems in place. CoPilot was also activated in Office365 to assist in writing and editing of the paper. All AI Systems have TRUTH MODE manually coded into the prompts, so no false information were produced and fact checked on all counts.

However, the candidate believes these three cohorts could be integrated into the overall master's program, allowing their skillsets to intersect and complement each other.

Research and Discussion on Possible Thesis Topics

Looking back at the subject matter, these are the best options proposed by the cohort and teaching staff for attempt, based on current capabilities, educational background and course outline:

AI-Driven Cybersecurity in Mobile & Wireless Systems
How AI/ML enhances anomaly detection, intrusion prevention, and resilience in wireless networks.
Data Science for Distributed Databases and Secure Data Management
Using predictive analytics, ML, and automated database management for secure, large-scale distributed data systems.
Intelligent Error-Correcting Codes and Cryptography
Application of AI to optimize coding algorithms (e.g., convolutional codes, Reed-Solomon) and cryptographic schemes for secure communication.

Brainstorming 3 possible topics

After much deliberation, the team members further refined and suggested the following topics for attempt:

AI-Powered Intrusion Detection Systems for Next-Generation Wireless Networks
Data Science Approaches to Secure and Efficient Distributed Database Management
Machine Learning Optimization of Error-Correcting Codes and Cryptographic Algorithms for Cybersecurity

Thesis Statements on the 3 possible topics

As candidate will be attempting a PhD Styled thesis, these thesis statements would be used to propose to the senate for the purpose of the attempted subjects.

Draft Statements for MS-PhD level Abstract and synopsis :

AI-Powered Intrusion Detection Systems for Next-Generation Wireless Networks
This thesis investigates the integration of machine learning models into intrusion detection systems (IDS) for 5G/6G wireless infrastructures, aiming to enhance anomaly detection accuracy and reduce false positives, thereby strengthening cybersecurity in mobile environments.
Data Science Approaches to Secure and Efficient Distributed Database Management
This research explores how advanced data science techniques, including predictive analytics and automated anomaly detection, can optimize performance, reliability, and security in distributed database systems handling sensitive and large-scale data.
Machine Learning Optimization of Error-Correcting Codes and Cryptographic Algorithms for Cybersecurity
This thesis develops AI-enhanced optimization models for coding algorithms and cryptographic schemes, with the goal of improving error resilience, throughput, and resistance against cyber threats in high-speed network communications.

END OF CHAPTER 1

Chapter 2 Literature Review

Each of the proposed topics were presented with a set of Literature Review and Directional input as to the how the research would be laid out.

Topic 1: AI-Powered Intrusion Detection in Wireless Networks

Foundations: Intrusion Detection Systems (IDS), anomaly vs. signature-based detection.
Current Research: ML in IDS (e.g., Random Forest, CNN, RNN for network traffic).
Gap: High false positive rates, adaptability in mobile/wireless contexts.
Key Authors/Journals: IEEE Communications Surveys, ACM Computing Surveys, Elsevier Computers & Security.

Topic 2: Data Science in Distributed Databases

Foundations: CAP theorem, distributed DB management (MongoDB, Cassandra, etc.).
Current Research: AI for query optimization, automated fault detection.
Gap: Balancing performance, security, and reliability in real-time.
Key Authors/Journals: VLDB Journal, IEEE Transactions on Knowledge and Data Engineering.

Topic 3: AI-Optimized Error-Correcting Codes & Cryptography

Foundations: Cyclic codes, convolutional codes, Reed-Solomon, block ciphers.
Current Research: AI for decoding optimization, cryptographic key prediction prevention.
Gap: Integrating ML with coding/crypto without introducing vulnerabilities.
Key Authors/Journals: IEEE Transactions on Information Theory, Journal of Cryptology.

However, the preliminary subject that I will be attempting will be mobile and wireless networks system as a primary subject.

Advisor also recommended to keep the wording to 12,000 words in context at the MSc level, and expand should I dedicate to have this paper be expanded further at the PhD level later on in the year.

The take on TOPIC 1 is good, but given the density of the research, it would be good to do it at the PhD Level, once this paper has been published on Google Scholar.

Background of candidate

About Rapheal Azrin

My Profile as a Regional IM/IT Digitalization & Technology Manager at Shell Petroleum provides me a vast knowledge of know-how in the field of telecommunications and I felt I should showcase my git into this paper.

Candidate is also an ICANN IANA Working Stakeholder member, collaborating for RFC and IEEE technical papers.

Candidate also has more than 30 years of experience in the relevant field, specializing in Web Hosting, Telecommunications and Intellectual Properties.

Something noteworthy is that the candidate an IEEE Chartered Fellow in the field of WDRP Domain Names and Dispute Arbitration, in the aspect of copyright & Intellectual Property infringement.

Public information can be gathered across search engines, Crunchbase and Linkedin.

https://www.linkedin.com/in/azrinmajid - Linkedin Profile

https://icannwiki.org/Raphael_Azrin - ICANN WIKI

https://www.crunchbase.com/person/rapheal-azrin - Crunchbase

He is also an advocate for crypto & digital assets in using blockchain as an encoding technology such as Blake17,Blake19 shuffle et al.

END OF CHAPTER 2

Chapter 3 - Abstract

This thesis presents a comprehensive, engineering-focused analysis of mobile and wireless radio systems, charting their evolution from analog cellular networks of the 1980s through the era of 5G New Radio and into the initial phases of 6G research. Emphasis is placed on the technical lineage of air interfaces—progressing from FDMA and analog FM to TDMA, CDMA, OFDMA, and massive MIMO—and situates these innovations within the broader contexts of spectrum allocation, propagation fundamentals, and regulatory policy frameworks across ASEAN, APAC, Europe, and ANZ.

Topological perspectives are integrated, encompassing macro, micro, PICO, FEMTO, LPWAN, ad hoc, and satellite constellation architectures. The study details the progression of core network technologies, from circuit-switched Mobile Switching Centers (MSCs) to 5G Service-Based Architectures (SBA) and the advent of AI-native cores. Security and authentication mechanisms receive special focus, tracing the shift from legacy analog protocols to modern SIM-based authentication, mutual AKA, eSIM provisioning, SUCI identifier protection, and quantum-safe, forward-looking approaches.

Comparative case studies of deployments in Singapore, Malaysia, Japan, and Europe illuminate how local geography, regulation, and socio-economic conditions have shaped divergent paths of technology adoption. Special systems—including TETRA for mission-critical voice, LoRaWAN for IoT, satellite backhaul (LEO/MEO/GEO), WiMAX, Wi-Fi, Bluetooth, ZigBee, and UWB—are examined for their technical and operational trade-offs. An addendum develops the Smart Nation case study, highlighting Singapore’s PSA Tuas Mega Port, autonomous vehicles, and urban IoT ecosystems as precursors for advanced research into smart infrastructures.

The thesis also addresses the integration of Artificial Intelligence and Data Science, covering predictive resource allocation, anomaly detection, self-optimizing networks (SON), and machine learning-driven security as key enablers for managing next-generation system complexity. Ultimately, the work argues that the progression of mobile and wireless systems is not merely a pursuit of speed, but a convergence of communication, computation, and control, underpinning the Fourth Industrial Revolution. Concluding with a forward-looking perspective, the thesis discusses developments in 5G-Advanced and 6G research, including sub-THz spectrum, reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), and post-quantum security, positioning this work as both a technical record and a strategic roadmap for future scholarly and industrial exploration.

Thesis Statement

This thesis critically examines the technical evolution, socio-economic implications, and prospective development of mobile and wireless radio systems, tracing their advancement from first-generation (1G) analog voice networks to contemporary 5G AI-native infrastructures and emerging 6G paradigms. It posits that mobile networks have transcended their original role as communication platforms, becoming essential digital infrastructure that underpins smart nations, industrial automation, and socio-economic growth. By integrating engineering analysis, comparative regional case studies across ASEAN, APAC, Europe, and ANZ, and perspectives from Artificial Intelligence and Data Science, this research demonstrates that the design and operation of wireless systems represent a convergence of spectrum policy, architectural innovation, and intelligent automation. The thesis contends that the future of wireless connectivity will be defined by AI-enhanced, secure, and context-aware architectures, capable of supporting both mass-market services and mission-critical applications, thereby establishing 5G-Advanced and 6G as foundational enablers of the Fourth Industrial Revolution.

Dedication and Thanks

I am dedicating this thesis to my beloved daughter, IZZATI MARDIAH AZRIN whom passed away on 10^th September 2025 while I am writing this paper. Although it was a crushing blow to my emotional state of mind and to our family during this darkest moments, I strive to carry on to complete this paper , in whatever way possible for the better of others. From Allah we come, To Him we return. Nothing happens without the will of God.

Candidate is appreciative for the support during these times to the following persons

JeyaShree Rajkumar – Senior Program Manager, Lithan Academy for the inputs on the usage of Artificial Intelligence, Prompting, Programming, Workflow designs which have been heavily utilized in this program and the presentation of the thesis.

Andrew Toh Tze Chow – Adjuct Faculty Lecturer - Artificial Intelligence Regression and Progression Modelling techniques https://www.linkedin.com/in/andrew-toh78/

Fan Yeng Loon – Adjuct Faculty Lecturer – Machine Learning Technical techniques crucial insights to the technological Data Science Analytics

Dhanabal T – Lithan Academy / UniMarconi Adjuct Lecturer – Basis of Encyrptions in Data Communications

Dr Azian Razak – Zyraz Technology – CXO – ZYRAZ Global Technologies – Scaleup systems in industrialised environments optimized using Artificial Intelligence , Machine Learning and Data Science Analytics. Linkedin : https://www.linkedin.com/in/azianrazak

Dr M Rezuwan Zakaria – Sentara group – Neste Oils – Usage of Machine Learning Modelling in real time, JIT systems from Research to full scale. https://www.linkedin.com/in/rezuwanzakaria

Also many thanks to fellow cohort mates of PDDS0524 of the Lithan Academy:

Mahathir Humaidi - Collaboration classmate & brainstorming of the subject matters – Program Manager, Lithan Academy

https://www.linkedin.com/in/mahathir-humaid

Aloysius Chia - Collaboration Classmate,AML/KYC Specialist – HSBC Singapore

https://linkedin.com/in/aloysiuschia

Justin Neo - Collaboration Classmate – Business Development – HSBC Singapore

https://www.linkedin.com/in/justin-neo-01140047

Jimmy Seah - Collaboration Classmate – Director – BECA

https://www.linkedin.com/in/jimmyseah/

Ng Yea Ling – Collaboration Classmate – PICO Creative

https://www.linkedin.com/in/yea-ling-ng-578779171/

Research & Scope

The evolution of mobile and wireless communication systems over the last four decades represents one of the most transformative engineering and societal achievements in human history. From the humble beginnings of analog cellular telephony in the 1980s (1G) to today’s highly sophisticated 5G networks and the research frontier of 6G, mobile and wireless technologies have fundamentally reshaped the global economy, connectivity paradigms, and the nature of human interaction. This thesis presents a comprehensive engineering-oriented overview of mobile and wireless radio systems, emphasizing their technical evolution, underlying architectures, and deployment topologies, while integrating perspectives from data science, artificial intelligence (AI), and machine learning (ML).

The study addresses both historical context and forward-looking perspectives, incorporating systems such as Wi-Fi, WiMAX, LoRaWAN, TETRA ATEX radios, ST GRID devices, and non-terrestrial networks including satellite constellations such as Beidou, Gouwang, Qianfan, Starlink, and Inmarsat. It also provides a comparative regional analysis focusing on ASEAN (Association of Southeast Asian Nations) and ANZ (Australia and New Zealand) markets, where diverse geographic, demographic, and regulatory conditions have shaped unique deployment trajectories.

The relevance of this topic is manifold. From an engineering standpoint, the radio technologies that underpin mobile networks illustrate the interplay between spectrum allocation, modulation techniques, hardware capabilities, and topological design. From a data science and AI perspective, modern networks have become too complex to manage solely through human intervention; they rely increasingly on autonomous optimization, predictive analytics, and AI-native architectures. From a socio-economic angle, mobile and wireless networks have become essential infrastructures—particularly evident during the COVID-19 pandemic when connectivity underpinned remote work, education, and healthcare.

Thus, this thesis aims to:

Trace the technical lineage of mobile and wireless systems from 1G to 5G and beyond, including parallel technologies such as Wi-Fi, WiMAX, and LPWAN.
Analyze the role of spectrum and regulation, with a specific emphasis on the historically significant 900 MHz band and spectrum management policies in ASEAN and ANZ.
Examine propagation fundamentals and how link budgets drive engineering design.
Investigate modulation, coding, and multiple access schemes as the building blocks of radio systems.
Compare network topologies and hierarchies, from macro-cells to femtocells, as well as mesh and ad hoc structures.
Explore specialized systems including mission-critical communications (TETRA ATEX, ST GRID) and satellite networks.
Assess the role of AI, ML, and Data Science in optimization, performance analysis, and next-generation innovations.
Provide regional case studies (ASEAN and ANZ), highlighting opportunities and challenges.
Conclude with a forward-looking view on 5G Advanced, 6G research themes, and the growing AI-native network paradigm.

Research Methodology

The methodology employed in this thesis adheres to a mixed qualitative–quantitative framework, designed to ensure both academic rigor and practical engineering applicability.

Literature Review and Bibliographic Analysis

Primary Sources:

3GPP specifications (TS 36.xxx for LTE, TS 38.xxx for 5G NR).
ITU reports (IMT-2000, IMT-Advanced, IMT-2020).
IEEE standards (802.11 Wi-Fi, 802.16 WiMAX).
LoRa Alliance technical documentation.

Secondary Sources:

Peer-reviewed academic journals (IEEE Transactions on Wireless Communications, Elsevier Computer Networks, ACM SIGCOMM).
Industry white papers (Ericsson, Nokia, Huawei, Qualcomm, Cisco).
Regulatory reports from ASEAN/ANZ agencies (e.g., ACMA in Australia, IMDA in Singapore, NTC in the Philippines).

Grey Literature:

Satellite operator documentation (Inmarsat technical manuals, Starlink deployment updates, Chinese satellite navigation whitepapers for Beidou/Gouwang/Qianfan).
Operator field reports (Singtel, Telstra, Globe Telecom).

The literature review ensures triangulation of perspectives: academic theory, industry practice, and regulatory frameworks.

Comparative Case Study Method

The thesis uses comparative case studies for ASEAN and ANZ to illustrate how geographic, economic, and regulatory differences influence mobile system deployment.

ASEAN: diverse spectrum of markets, from advanced (Singapore) to developing (Cambodia, Myanmar). Key themes: urban-rural divide, affordability, spectrum scarcity.
ANZ: large land masses, low population density, leading to emphasis on satellite integration and rural coverage.

Case studies will be supported by quantitative data (coverage, latency, throughput, spectrum allocation) and qualitative analysis (policy, regulation, operator strategies).

Engineering Analysis

The thesis employs engineering evaluation methods, including:

Link budget modeling: examining transmit power, antenna gains, path loss models (COST-Hata, ITU-R).
Spectral efficiency comparisons across generations (bits/s/Hz).
Latency and throughput trade-offs in different access schemes (TDMA vs CDMA vs OFDMA).
QoS/KPI assessment with AI-based predictive modeling (supervised ML for anomaly detection, clustering for traffic profiling).

AI and Data Science Integration

Modern networks rely heavily on AI/ML techniques. This thesis incorporates:

AI-native RAN (Radio Access Networks): Machine learning for beam management, anomaly detection, and interference prediction.
Data Science for KPI Optimization: Predictive analytics for handover success rates, coverage holes, and traffic distribution.
Big Data in Core Networks: Cloud-native 5G cores leveraging ML-based orchestration.
AI-enhanced Security: ML-based intrusion detection and adaptive encryption.

Validation

To ensure validity and reliability:

Cross-verification of technical details with multiple sources (academic + industry + regulatory).
Quantitative benchmarks aligned with ITU/3GPP standards.
Transparency in assumptions (e.g., propagation models assume urban macro-cell unless stated otherwise).
Clear citation and footnoting, following UniMarconi’s guidelines.

Limitations

Rapid pace of 5G/6G research may render certain projections outdated.
Some proprietary satellite system details are not publicly disclosed.
Case studies rely on available regulatory/industry data, which may not be fully standardized across regions.

Thesis research is primarily focused on the APAC-ASEAN, specifically Singapore, Malaysia, Japan, ANZ clustering with some distinctive comparative legislations against the Euro EEC and Americas
Machine Learning bias may be implied in the research as agentic and deep research systems were used to derive the deep dive data.

END OF CHAPTER 3

Chapter 4 - Historical timeline – 1G to 5G and Beyond

Introduction to the generational Evolution

The Evolution of Mobile and Wireless communication networks were mainly defined by the CCITT or Consultative Committee for International Telegraphs and Telephone, based out of France and later on merged into the ITU or International Telecommunications Union (www.itu.int) which is now based out of Geneva, Swiss.

The earliest way of communicating was using MORSE CODE through low band frequencies across the Atlantic thanks to WW2 where early German Enigma machine cause heavy casualties to the allied forces.

The evolution of mobile communication networks from the 1980s to the present has been marked by a sequence of “generational leaps” defined by standardization bodies such as the International Telecommunication Union (ITU) and the 3rd Generation Partnership Project (3GPP). Each generation (1G, 2G, 3G, 4G, and 5G) introduced new radio access technologies, modulation schemes, network architectures, and service paradigms, with 6G already emerging as a research frontier. These generational shifts were not merely technological; they were shaped by regulation, spectrum availability, socio-economic factors, and hardware capabilities.

The following sections trace this timeline in detail, highlighting technical characteristics, socio-economic impacts, and the role of AI and data science in shaping or analyzing each phase.

4.1 First Generation (1G): The Analog Era (1981–1991)

4.1.1 Overview

1G systems marked the beginning of cellular telephony, replacing limited-range radio phones with large-scale mobile systems. They were fully analog, primarily designed for voice services, and lacked encryption or advanced mobility features.

4.1.2 Key Standards

AMPS (Advanced Mobile Phone System) – deployed in North America, operating in the 800 MHz band with 30 kHz FM channels.

NMT (Nordic Mobile Telephony) – Scandinavia, 450/900 MHz bands.

TACS (Total Access Communication System) – UK, Italy, other European countries.

4.1.3 Technical Features

Multiple Access: FDMA (Frequency Division Multiple Access).

Bandwidth Efficiency: <0.5 bits/s/Hz.

Handover: Hard handoff, circuit-switched connections.

Security: Minimal; cloning and interception common.

4.1.4 Limitations

· Very low capacity relative to spectrum usage.

· No data services, SMS, or encryption.

· Expensive handsets with limited battery life.

4.2 Second Generation (2G++): Digital Cellular (1991–2001)

4.2.1 Overview

2G introduced digital modulation, which enabled greater spectral efficiency, voice encryption, and SMS services. It was the first generation where mobile phones became mass-market consumer devices.

4.2.2 Key Standards

GSM (Global System for Mobile Communications) – TDMA-based, widely deployed in Europe, Asia, Africa. Operated primarily in 900/1800 MHz bands.

IS-95 (cdmaOne) – First commercial CDMA system, pioneered by Qualcomm, operating in the US.

IS-136 (D-AMPS) – North American digital TDMA system, less successful than GSM or CDMA.

4.2.3 Technical Features

Multiple Access: TDMA (GSM), CDMA (IS-95).

Encryption: A5/1 cipher in GSM, improved security vs. 1G.

Data Services: GPRS (General Packet Radio Service) → “2.5G”, EDGE (Enhanced Data for GSM Evolution) → “2.75G”.

Peak Speeds: 50–200 kbps.

4.2.4 Socio-Economic Impact

SMS revolutionized communication; low-cost and widely adopted.

Mobile penetration grew rapidly in ASEAN (e.g., Philippines’ “texting capital” phenomenon).

In ANZ, 2G enabled widespread rural voice coverage.

4.2.5 GPRS & EDGE

Typically known as 2.5G and 2.75G, both Global Packet Radio Service and Enhanced Data for GSM Evolution plays a pivotal role in the next step of evolution into 3G networks. It also enables Laptops (aka Mobile Computers) to be really mobile so that it can be connected to the internet. Usage includes wireless Credit Card EFTPOS devices and Taxi Booking MDT. (shown: MDT POS system for Comfort Taxi in Singapore, circa 2000s)

4.3 Third Generation (3G): Multimedia & Internet (2001–2010)

4.3.1 Overview

3G was standardized under ITU’s IMT-2000 umbrella, promising global roaming and broadband-like services.

4.3.2 Key Standards

UMTS/W-CDMA – Europe and Asia, 5 MHz carriers.

CDMA2000 (1xRTT, EV-DO) – North America and parts of Asia-Pacific.

4.3.3 Technical Features

Multiple Access: Wideband CDMA (UMTS), CDMA2000.

Peak Speeds:

o UMTS ~384 kbps,

o HSPA (High Speed Packet Access) up to 14 Mbps (later HSPA+).

Core Network: Introduction of packet-switched cores alongside circuit-switched.

4.3.4 Limitations

High cost of licenses in some countries (e.g., Europe).

Spectrum fragmentation slowed adoption in ASEAN.

ANZ markets adopted 3G early (Telstra’s Next G network).

Different Networks not cross compatible between US and Rest of World.

4.4 Fourth Generation (4G): The IP Broadband Era (2009–2019)

4.4.1 Overview

4G represented a paradigm shift: all-IP networks, high-speed broadband, and LTE as the global standard.

4.4.2 Key Standards

LTE (Long Term Evolution) – OFDMA downlink, SC-FDMA uplink.

WiMAX (802.16e/m) – Considered 4G by ITU, but commercially outcompeted by LTE.

4.4.3 Technical Features

Multiple Access: OFDMA (downlink), SC-FDMA (uplink).

Bandwidth: Up to 20 MHz per carrier; carrier aggregation up to 100 MHz.

Peak Speeds: 100 Mbps (Release 8 LTE), later >1 Gbps with LTE-Advanced.

Core Network: Evolved Packet Core (EPC), all-IP, IMS/VoLTE for voice.

4.4.4 Impact

Enabled mobile broadband, video streaming, app economy.

ASEAN: accelerated smartphone adoption, mobile financial inclusion.

ANZ: Telstra and Optus pioneered early LTE rollouts for rural broadband.

4.5 Fifth Generation (5G): Ultra-Flexible Architecture (2019–Present)

4.5.1 Overview

5G is not just a faster network but a flexible, software-defined platform supporting enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC).

4.5.2 Key Standards

3GPP Release 15 (2019) – 5G NR, NSA/SA modes.

Release 16–17 – URLLC, industrial IoT, non-terrestrial networks (NTN).

Release 18 (5G Advanced) – AI-native RAN, enhanced XR, positioning.

4.5.3 Technical Features

Multiple Access: Flexible OFDM numerologies (15–240 kHz spacing).

Spectrum:

o FR1 (sub-6 GHz, e.g., 3.5 GHz).

o FR2 (mmWave, 24–40+ GHz).

Peak Speeds: 10 Gbps in lab trials.

Latency: <1 ms target for URLLC.

MIMO: Massive MIMO (64T64R, >256 antennas), beamforming.

4.5.4 Use Cases

Smart cities, autonomous vehicles, industrial automation.

ASEAN: Singapore leads with 5G SA, others catching up.

ANZ: Focus on industrial IoT and rural coverage with low-band 5G.

4.6 Towards Sixth Generation (6G): The Research Frontier (2030+)

4.6.1 Emerging Concepts

Sub-THz spectrum (100 GHz–1 THz) for Tbps data rates.( Terabits)

Joint Communication and Sensing (JCAS).

Reconfigurable Intelligent Surfaces (RIS).

Integrated terrestrial–non-terrestrial networks (NTN).

AI-native core and RAN.

4.6.2 Global Research Initiatives

Europe: Hexa-X program.

China: 6G test satellite launches.

Japan/Korea: Government-industry consortia for 6G by 2030.

ASEAN/ANZ: Early research but more focused on 5G rollout.

4.7 Summary

From analog voice (1G) to AI-native, cloud-driven architectures (5G and beyond), the mobile communications timeline reflects both technical ingenuity and socio-economic drivers. The integration of AI and Data Science is not a late addition but a necessary response to the complexity of modern networks, paving the way for 6G as a cognitive, sensing-aware infrastructure.

END OF CHAPTER 4

Chapter 5 - Spectrum and Regulation (ASEAN/APAC Focus, 900 MHz and Beyond)

5.1 Introduction to Spectrum as a Resource

Spectrum is the most fundamental and limited natural resource in wireless communications. It is not consumable like fuel, but it is scarce and subject to strict allocation, regulation, and reuse strategies. Unlike other resources, spectrum cannot be manufactured; it must be efficiently shared and reused using frequency planning, modulation efficiency, and multi-access schemes.

The management of spectrum has been overseen globally by the International Telecommunication Union (ITU), while national regulators such as ACMA (Australian Communications and Media Authority), IMDA (Singapore), NBTC (Thailand), and others in the ASEAN region enforce localized frameworks. Spectrum licensing, auctions, and refarming are key mechanisms through which governments allocate frequencies to operators.

5.2 The Importance of the 900 MHz Band

The 900 MHz band has historically played a central role in mobile communications:

1G: Many early analog systems operated at 900 MHz.
2G GSM: 900 MHz was the global anchor band, offering balance between coverage and capacity.
3G UMTS: Some refarming allowed deployment of 3G in 900 MHz.
4G LTE: Operators in ASEAN/ANZ reused 900 MHz for LTE coverage in rural areas.
5G NR: While primarily sub-6 GHz and mmWave, some early deployments use refarmed 900 MHz for coverage-layer NR.

5.3 Propagation Benefits

Lower path loss vs. higher bands.
Deeper indoor penetration.
Larger cell radius → fewer base stations needed, lowering CAPEX.

5.4 Trade-offs

Narrower bandwidth compared to higher frequencies (limiting capacity).
Heavily congested in many regions due to legacy users.

5.5 Spectrum Allocation by Generation

Generation	Typical Bands	Bandwidth Per Carrier	Notes
1G	800–900 MHz	25–30 kHz	Analog FDMA channels
2G (GSM)	900, 1800, 1900 MHz	200 kHz	900 MHz dominant in ASEAN/ANZ
3G (UMTS/ W-CDMA)	2.1 GHz (primary), 900 MHz (refarmed)	5 MHz	Used dual bands
4G (LTE)	700, 800, 900, 1800, 2600, 3500 MHz	1.4–20 MHz (CA up to 100 MHz)	ASEAN/ANZ diversified
5G (NR)	FR1 (600–4200 MHz), FR2 (24–40 GHz)	5–100 MHz (FR1), 100–400 MHz (FR2)	Auctions across ANZ/ASEAN ongoing
6G (Future)	Sub-THz (100–1000 GHz)	2–10 GHz	Research phase only

5.6 Spectrum Licensing Approaches

5.6.1 Auction-Based Allocation

Common in ANZ (e.g., Australian 3.6 GHz 5G auction, 2018).
Generates government revenue but may increase operator costs → higher end-user tariffs.

5.6.2 Administrative Assignment

Seen in some ASEAN countries with less mature regulatory markets.
Spectrum directly assigned to incumbents.

5.6.3 Unlicensed Spectrum

ISM bands (2.4 GHz, 5 GHz, 6 GHz) widely used for Wi-Fi and LoRaWAN.
ASEAN has mixed adoption of 6 GHz Wi-Fi 6E.
ANZ allows wider unlicensed use due to advanced regulatory frameworks.

5.6.4 Shared Spectrum Models

CBRS (Citizens Broadband Radio Service)-style models in the US inspire ASEAN/ANZ regulators.
Potential use of shared spectrum for private 5G/IoT deployments.

5.7 Spectrum Refarming

Refarming is essential to migrate legacy networks without wasting spectrum:

ASEAN Example: Philippines’ Globe Telecom reallocated 2G/3G bands to LTE and now to 5G.
ANZ Example: Telstra and Optus shut down 2G by 2016–2017, refarming spectrum to LTE.

Refarming balances backward compatibility with forward innovation.

5.7.1 ASEAN Spectrum Landscape

ASEAN nations are diverse in spectrum usage:

Singapore (IMDA): Aggressive 5G rollout in 3.5 GHz and mmWave. 900 MHz used for LTE fallback.

Malaysia (MCMC): Single Wholesale Network for 5G (controversial).

Thailand (NBTC): Multi-band 5G auctions (700 MHz, 2600 MHz, 26 GHz).

Indonesia: Large geography, limited 3.5 GHz spectrum availability → reliance on 1800/2100 MHz refarming.

Philippines: New entrant Dito Telecommunity licensed in 700 MHz for coverage.

Challenges:

Fragmented policies across countries.

High auction fees vs. affordability pressures.

Legacy 2G networks still operational in some regions.

5.7.2 ANZ Spectrum Landscape

Australia and New Zealand benefit from more coordinated spectrum planning.

Australia (ACMA):

o 700 MHz (“digital dividend”) allocated for LTE and 5G.

o 3.6 GHz allocated to 5G.

o mmWave auctions (26 GHz) completed in 2021.

New Zealand (MBIE):

o 600 MHz, 700 MHz for rural LTE/5G.

o 3.5 GHz as the primary 5G band.

Unique features:

Large landmass → heavy reliance on low-band spectrum for rural coverage.

Early adoption of satellite augmentation (Starlink, Inmarsat) to fill coverage gaps.

5.8 Sub-THz and mmWave Challenges

While mmWave (24–40 GHz) offers extreme bandwidth, coverage limitations are significant:

Propagation Loss: Free-space path loss increases 6 dB per frequency doubling.
Penetration Loss: High absorption through walls, foliage, even rain.
Deployment Cost: Requires dense small-cell infrastructure.

ASEAN context: Dense cities like Singapore may benefit. Rural ASEAN less suitable.
ANZ context: Limited mmWave beyond urban hotspots due to geography.

Sub-THz for 6G introduces further challenges: atmospheric absorption, hardware cost, regulatory uncertainties.

5.9 Spectrum Policy Trends

Harmonization: ITU encourages global harmonization (e.g., 3.5 GHz for 5G), but national variations persist.
Dynamic Spectrum Sharing (DSS): Used to allow LTE and 5G coexistence in same band.
Satellite-Terrestrial Convergence: New ITU allocations for non-terrestrial networks (NTN) under 5G NR.
AI for Spectrum Management: Cognitive radio and ML-driven spectrum sensing proposed for 6G.

5.10 Summary

Spectrum regulation has been both an enabler and bottleneck of mobile technology evolution. The 900 MHz band exemplifies the continuity of spectrum reuse, while higher bands (3.5 GHz, mmWave) reflect the demand for capacity. ASEAN’s fragmented policies contrast with ANZ’s coordinated planning, yet both regions demonstrate innovation in refarming, auctions, and satellite integration.

AI and Data Science will increasingly be used for dynamic spectrum allocation, interference detection, and demand prediction, making spectrum management not only a regulatory challenge but also a computational one.

END OF CHAPTER 5

Chapter 5.11 AI and ML for Propagation Prediction

Traditional models are deterministic/empirical. AI/ML methods use real measurement data:

Supervised ML: Train models on drive-test/KPI data to predict RSRP/RSRQ.
Deep Learning: CNNs for RF fingerprinting.
Reinforcement Learning: Adaptive beam alignment in mmWave links.
Big Data Analytics: Correlating traffic patterns with propagation anomalies.

Operators in Singapore and Australia have trialed AI for automatic coverage hole detection.

Summary

Radio propagation and link budgets are the foundation of mobile system design. Lower frequencies (e.g., 900 MHz) provide wide-area coverage, while higher frequencies (e.g., mmWave) require AI-optimized dense deployments. Modern operators cannot rely solely on mathematical models—they require data-driven, AI-augmented approaches to continuously optimize networks in real time.

Chapter 6 – Modulation, Coding, and Multiple Access Evolution

6.1 Introduction

The evolution of modulation, coding, and multiple access techniques has been the engine driving improvements in capacity, spectral efficiency, and reliability across successive mobile generations. Each leap in wireless standards has corresponded to advancements in how bits are represented on radio waves and how multiple users are multiplexed onto finite spectrum.

This chapter explores these advancements chronologically, from analog FM in 1G to OFDMA and advanced coding in 5G, while considering the growing role of AI and ML in waveform optimization, adaptive coding, and interference management.

6.2 Modulation Evolution

1G: Analog FM

Frequency Modulation (FM): Simple but inefficient.
Channel bandwidth: 25–30 kHz.
Susceptible to interference, poor spectral efficiency.

2G: Digital Modulation

GMSK (Gaussian Minimum Shift Keying) in GSM.

Constant envelope → efficient power amplifiers.
Robust but limited spectral efficiency (~1 bit/s/Hz).

π/4-QPSK in IS-54 TDMA.
QPSK/BPSK/CDMA spreading in IS-95.

2.5G and 3G

8-PSK in EDGE → 3 bits/symbol.
QPSK, 16-QAM, 64-QAM in W-CDMA/HSPA.
Adaptive modulation introduced: switching based on channel conditions.

4G LTE (Long Term Evolution)

OFDMA (downlink): Combats multipath with cyclic prefix.
SC-FDMA (uplink): Reduces Peak-to-Average Power Ratio (PAPR).
Adaptive modulation up to 256-QAM.

5G NR

Flexible numerologies (subcarrier spacing 15–240 kHz).
Higher-order QAM (up to 1024-QAM in trials).
Beamformed modulation optimized for spatial channels.

6G Outlook

Orbital Angular Momentum (OAM) waves under research.
AI-designed waveforms: Neural networks optimize modulation schemes dynamically.

6.3 Channel Coding Evolution

Error-correcting codes have evolved alongside modulation, crucial for reliability under fading and interference.

1G: No channel coding.
2G GSM: Convolutional coding + interleaving.
3G UMTS: Turbo codes → near Shannon-limit performance.
4G LTE: Turbo codes (legacy), later LDPC (in trials).
5G NR:

LDPC (Low-Density Parity Check): For data channels.
Polar Codes: For control channels.

6G (Research): Sparse graph codes, AI-assisted error correction.

6.4 Multiple Access Techniques

1G – FDMA – Frequency Divided Multiple Access Technology

Users separated by frequency.
Simple but inefficient, poor scalability.

2G – TDMA & CDMA – Time Divided & Code Division Multiple-Access

TDMA (Time Division Multiple Access):

GSM: 200 kHz channels, 8 time slots.
Effective for circuit-switched voice.

CDMA (Code Division Multiple Access):

IS-95, UMTS.
Spreads signals with pseudo-random codes.
Soft handoffs, interference-limited planning.

3G – W-CDMA

Wideband CDMA, 5 MHz carriers.
Supported higher data rates but suffered from self-interference.

OFDMA

Orthogonal Frequency Division Multiple Access.
Divides spectrum into orthogonal subcarriers.
Flexible scheduling in time–frequency grid.
Multipath resilience, high spectral efficiency.

5G NR – Flexible OFDM

Variable subcarrier spacing.
OFDM extended to mmWave, massive MIMO.
Sidelink and non-terrestrial integration.
Support for URLLC (low latency), mMTC (IoT), eMBB (broadband).

6G Outlook

NOMA (Non-Orthogonal Multiple Access):

Users multiplexed by power domain.
ML used for successive interference cancellation.

Rate-Splitting Multiple Access (RSMA).
AI-driven resource allocation predicted to dominate.

6.5 Synchronization and Reference Signals

Modern systems require precise synchronization for coherent detection.

2G GSM: Broadcast Control Channel (BCCH).
3G: Primary/Secondary Synchronization Channels.
4G LTE: Primary/Secondary Sync Signals, Cell-specific Reference Signals.
5G NR:

PSS/SSS for cell search.
DM-RS/CSI-RS for channel estimation in beamformed channels.

AI-enhanced synchronization techniques are being explored, where ML algorithms dynamically track Doppler and fading.

6.6 AI and Data Science in Modulation and Coding

Adaptive Modulation & Coding (AMC): Traditional link adaptation uses SNR (Signal Noise Ratio) thresholds. AI-based methods optimize multi-dimensional trade-offs (throughput, reliability, latency).
Deep Learning for Decoding: Neural decoders outperform LDPC in some cases.
Reinforcement Learning for Multiple Access: Used for NOMA power allocation.
Waveform Design: AI models search modulation/coding combinations beyond human-designed standards.

6.7 Summary

From simple analog FM (1G) to AI-optimized OFDM and coding in 5G and beyond, modulation and multiple access have been central to increasing capacity and efficiency. While human-designed techniques dominated until 5G, the 6G era will increasingly rely on AI to co-design and optimize waveforms, coding, and access schemes in real time.

END OF CHAPTER 6

Chapter 7 - Network Topologies and Hierarchies

7.1 Introduction

A mobile network is not only defined by its air interface or modulation scheme but also by its topology — the physical and logical arrangement of base stations, cells, and user devices. Over time, as demand for coverage, capacity, and reliability has increased, mobile networks have evolved from simple macrocellular deployments to heterogeneous networks (HetNets) integrating macro, micro, pico, femto cells, mesh, and device-to-device (D2D) topologies.

In parallel, Low-Power Wide Area Networks (LPWANs) such as LoRaWAN introduced a different topology optimized for low data rate, long-range IoT connectivity.

This chapter examines the evolution of topologies, their trade-offs, and the role of AI and data science in self-organizing networks (SONs) and autonomous topology management.

7.2 Macrocellular Topology

Definition

Macrocells are large coverage areas served by high-power base stations, typically transmitting at 20–40 W, mounted on towers or rooftops.

Characteristics

Coverage radius: 2–35 km depending on frequency and terrain.
Primary layer of 1G–4G deployments.
Cost-effective for rural areas (ANZ’s vast geography).

Limitations

Inefficient for indoor coverage in dense urban environments.
High inter-cell interference in congested networks.

7.3 Microcells

Definition

Smaller cells (100 m – 2 km radius) deployed for capacity enhancement in high-traffic areas.

Use Cases

Urban hotspots (shopping malls, downtown cores).
Supplement macro coverage in ASEAN megacities like Bangkok, Manila, Jakarta.

Trade-offs

Lower transmit power → lower CAPEX per site.
Increased OPEX due to denser site requirements.

6.4 Picocells

Definition

Very small base stations with coverage radius of 10–200 meters.

Use Cases

Enterprises, office buildings, airports.
Often used for indoor LTE/5G private networks.

ASEAN/ANZ Deployment

Singapore uses picocells for indoor 5G coverage in MRT tunnels.
Australia: mining companies deploy picocells for private LTE in remote facilities.

6.5 Femtocells

Definition

Ultra-small, consumer-deployed base stations (coverage radius 10–50 m).

Role

Extend indoor coverage.
Use residential broadband as backhaul.

Challenges

Interference management.
Security of user-deployed nodes.

6.6 Heterogeneous Networks (HetNets)

HetNets combine macro, micro, pico, and femto cells into a layered architecture.

Benefits

Macro layer ensures coverage.
Small cells increase localized capacity.
Offloading traffic improves spectrum efficiency.

6.7 SON (Self-Organizing Networks)

Automates configuration and optimization.
AI-based SONs adjust neighbor relations, power levels, and handover thresholds dynamically.
Widely used in 4G/5G deployments in ANZ (Telstra’s AI-driven SON pilot).

6.8 Mesh and Ad Hoc Networks

Public Safety and Tactical Use

TETRA systems use mesh/ad hoc extensions for first responders. (Used in Singapore for Oil & Gas, specific to Bukom & Jurong Islands)
ProSe (Proximity Services): Introduced in LTE Rel-12 for device-to-device.

Military and Industrial IoT

Mesh networks in mining (Australia) and oil rigs (Malaysia).
D2D communication critical for autonomous vehicles.

Challenges

Interference and scalability.
Security of decentralized topologies.

6.9 LPWAN Topologies

LoRaWAN – Low power Long Range Wide Area Network

Star-of-stars topology: end devices → gateways → network server.

Gateways connect to central servers via IP backhaul.

Optimized for long-range, low-power IoT telemetry.

Alternatives

Sigfox: Ultra-narrowband star topology.
NB-IoT / LTE-M: Cellular LPWAN alternatives.

ASEAN/ANZ Adoption

Singapore: nationwide LoRaWAN IoT deployments for smart city.

New Zealand: farmers use LoRaWAN for livestock monitoring.

6.10 AI/ML in Topology Optimization

Cell Planning

AI models trained on traffic data predict where small cells should be deployed.
Reinforcement learning for automatic tilt optimization of antennas.

Traffic Load Balancing

Clustering algorithms (k-means, DBSCAN) detect overloaded cells.
Load shifted via AI-controlled handover tuning.

Failure Recovery

Predictive maintenance using anomaly detection.
Self-healing topologies reroute traffic around failed nodes.

6.11 Comparative Topology Trade-offs

Topology	Coverage Radius	Strengths	Limitations
Macrocell	2–35 km	Wide area coverage, cost-effective	Poor indoor penetration
Microcell	0.1–2 km	Capacity boost, urban use	Higher site density
Picocell	10–200 m	Indoor coverage, enterprise	CAPEX for enterprise
Femtocell	<50 m	Consumer indoor use	Security, interference
Mesh/Ad Hoc	Varies	Resilient, decentralized	Complexity, interference

6.12 Summary

Network topologies evolved from macro-only 1G deployments to sophisticated multi-layered 5G HetNets and specialized IoT topologies. In ASEAN, dense cities require pico/femto deployments, while ANZ emphasizes macrocells and satellite integration.

AI and Data Science enable dynamic optimization of these topologies, ensuring real-time adaptability to changing traffic, failures, and propagation environments.

END OF CHAPTER 6

Chapter 7 - Standards by Generation

(1G to 5G, Wi-Fi, WiMAX, LoRaWAN)

7.1 Introduction

Telecommunications systems evolve not in isolation but under the guidance of standards bodies, which ensure interoperability, economies of scale, and global compatibility. The most influential standards bodies are:

3GPP (3rd Generation Partnership Project): LTE, 5G NR, core networks.
ITU (International Telecommunication Union): Defines IMT-2000, IMT-Advanced, IMT-2020 (frameworks for 3G, 4G, 5G).
IEEE (Institute of Electrical and Electronics Engineers): Wi-Fi (802.11), WiMAX (802.16).
LoRa Alliance: LoRaWAN standardization for LPWAN.

This chapter provides a generation-by-generation breakdown of standards, followed by Wi-Fi/WiMAX and LoRaWAN ecosystems.

7.2 1G Standards (Analog Cellular)

AMPS (Advanced Mobile Phone System)

Launched in the US in 1983.
30 kHz FM channels in the 800 MHz band.
FDMA, no encryption, poor spectral efficiency.

NMT (Nordic Mobile Telephony) – NoKIA/NorTEL

First international cellular standard (Scandinavia, early 1980s).
450/900 MHz bands.
Influenced global adoption.

TACS (Total Access Communication System)

UK adaptation of AMPS.
Widely used in Europe and Asia before GSM.

Summary: 1G standards were fragmented, regional, and analog-only.

7.3 2G Standards (Digital Cellular)

GSM (Global System for Mobile Communications)

ETSI standard (Europe, 1991).
TDMA, 200 kHz carriers.
Global adoption, especially in ASEAN/ANZ.
Introduced SMS and encryption.

IS-95 (cdmaOne)

Qualcomm’s CDMA-based system.
Spread spectrum, soft handoffs.
Popular in US, Korea.

IS-136 (D-AMPS)

US TDMA system.
Transitional, eventually replaced by GSM/CDMA.

2.5G Enhancements

GPRS: Packet-switched data (~50 kbps).
EDGE: Enhanced modulation (8-PSK), up to ~200 kbps.

7.4 3G Standards (IMT-2000)

UMTS / W-CDMA

Europe and Asia.
5 MHz carriers, 2.1 GHz band.
HSPA/HSPA+ → up to 42 Mbps.

CDMA2000

Evolution of IS-95.
1xRTT, EV-DO.
Deployed in North America, some Asian markets.

Regional Adoption

ASEAN: uneven rollout due to cost.
ANZ: Telstra’s “Next G” (850 MHz) was one of the most advanced UMTS networks globally.

7.5 4G Standards (IMT-Advanced)

LTE (Long Term Evolution)

3GPP Release 8 (2009).
OFDMA (DL), SC-FDMA (UL).
Up to 20 MHz per carrier; carrier aggregation (CA) enabled multi-carrier operation.
LTE-Advanced (Rel-10+):

Higher-order MIMO (8x8).
Coordinated multipoint (CoMP).
Peak rates >1 Gbps.

WiMAX (IEEE 802.16e/m)

Considered 4G by ITU, but lost to LTE due to ecosystem dominance.
Still deployed in some ASEAN regions for fixed broadband.

7.6 5G Standards (IMT-2020)

3GPP Release 15–17

5G NR (New Radio).
Flexible numerology (15–240 kHz spacing).
Sub-6 GHz (FR1) and mmWave (FR2).
Massive MIMO, beamforming.
NSA (non-standalone, LTE anchor) and SA (standalone, 5G core).

Release 18 and 5G Advanced

AI-native RAN.
Enhanced XR (Extended Reality) support.
RedCap (Reduced Capability) devices for IoT.
Non-terrestrial networks (NTN): satellites integrated with 5G.

7.7 Wi-Fi Standards (IEEE 802.11 Family)

Generation	Standard	Year	Features
Wi-Fi 1	802.11b	1999	DSSS, 2.4 GHz, 11 Mbps
Wi-Fi 2	802.11a	1999	OFDM, 5 GHz, 54 Mbps
Wi-Fi 3	802.11g	2003	2.4 GHz, OFDM, 54 Mbps
Wi-Fi 4	802.11n	2009	MIMO, 2.4/5 GHz, 600 Mbps
Wi-Fi 5	802.11ac	2014	MU-MIMO, 160 MHz channels, Gb/s
Wi-Fi 6	802.11ax	2019	OFDMA, BSS Coloring, low latency
Wi-Fi 7	802.11be	2024 (ongoing)	320 MHz channels, multi-link operation (MLO)

ASEAN/ANZ: Wi-Fi complements cellular for indoor connectivity; Wi-Fi 6E in 6 GHz is adopted in Australia and Singapore.

LoRaWAN (Low-Power Wide Area Network)

Characteristics

Physical Layer: LoRa modulation (chirp spread spectrum).
Topology: Star-of-stars (end devices → gateways → network servers).
Bands: ISM (868/915 MHz in ANZ/ASEAN).

Device Classes

Class A: Ultra-low power, scheduled downlinks.
Class B: Periodic beacon windows.
Class C: Almost continuous receive.

Deployment

ASEAN: Smart city (Singapore, Malaysia).
ANZ: Agricultural IoT (livestock, irrigation monitoring).

7.8 Comparative Technology Matrix

Technology	Access Method	Peak Data Rate	Latency	Strengths	Limitations
GSM/EDGE	TDMA	~0.2 Mbps	~150 ms	Wide coverage	Low data rates
UMTS/HSPA	W-CDMA	14–42 Mbps	50–100 ms	Proven tech	Interference mgmt
LTE-A	OFDMA/SC-FDMA	>1 Gbps	20–50 ms	Ecosystem, efficiency	Dense planning
5G NR	OFDMA	>10 Gbps	<1 ms	Flexible, slicing, URLLC	Complexity, mmWave limits
Wi-Fi 6/7	OFDMA	Multi-Gbps	~10 ms	Unlicensed, cheap	Contention, interference
LoRaWAN	ALOHA-like	kbps	Seconds	Long range, ultra-low power	Very low throughput

7.9 Summary

Standards have progressed from analog fragmentation (1G) to globally harmonized digital ecosystems (5G NR). Wi-Fi and LoRaWAN evolved in parallel, targeting different niches: Wi-Fi for indoor broadband and LoRaWAN for ultra-low power IoT.

The unifying trend is toward flexible, AI-native, software-defined standards where modulation, access, and resource allocation can adapt in real time. This lays the groundwork for 6G, where AI will likely play a direct role in waveform and standard evolution.

END OF CHAPTER 7

Chapter 8 - Specialized Systems & Use Cases

(LoRaWAN, TETRA ATEX, ST GRID, Satellites: Beidou, Gouwang, Qianfan, Starlink, Inmarsat)

8.1 Introduction

Beyond mainstream mobile standards (1G–5G), specialized wireless systems have emerged to meet unique requirements:

Low-power IoT (LoRaWAN).
Mission-critical communications (TETRA ATEX, ST GRID).
Satellite-based systems (Beidou, Gouwang, Qianfan, Starlink, Inmarsat).

These systems fill niches not well served by cellular broadband: industrial safety, rural connectivity, global positioning, maritime/aviation communications, and massive IoT telemetry.

8.2 LoRaWAN (Low Power, Long Range IoT)

Technical Overview

LoRa (physical layer): Chirp spread spectrum modulation.
LoRaWAN (protocol): Defines MAC and backend.
Bands: ISM (EU 868 MHz, US/AU 915 MHz, ASEAN varies).
Data rate: 0.3–50 kbps.
Range: 2–15 km urban, >50 km rural line-of-sight.
Battery life: 5–10 years.

Use Cases

Smart cities: parking sensors, waste management.
Agriculture: soil moisture, livestock tracking (ANZ).
Disaster response: temporary sensor networks.

Pros and Cons

✅ Long range, ultra-low power, unlicensed spectrum.
❌ Low throughput, high latency (seconds).

8.3 TETRA ATEX Systems

TETRA (Terrestrial Trunked Radio)

ETSI standard for professional mobile radio.
Used by police, emergency services, utilities.
Digital trunking, encryption, direct mode (D2D).
446Mhz in Singapore

ATEX Compliance

Devices certified for explosive environments (oil rigs, chemical plants, mining).
Ruggedized walkie-talkies with intrinsically safe design.

ASEAN/ANZ Deployment

ASEAN: TETRA used in airports, seaports, public safety (Singapore Police, Jakarta MRT).
ANZ: ATEX radios in mining (Western Australia, Queensland coal sector).

Pros and Cons

✅ Mission-critical reliability, D2D, encrypted.
❌ Limited bandwidth, not suited for broadband data. Transmitter is ranged up to 10km radius

8.4 - ST GRID Walkie-Talkie Phone

Overview

Hybrid of mobile phone and push-to-talk (PTT) radio.
Developed for industrial/public safety environments.
Operates on both cellular and dedicated PMR spectrum. 800/850x Mhz Freq in Singapore

Features

Group calls with near-zero latency.
Ruggedized design.
Integration with LTE mission-critical push-to-talk (MCPTT).

ASEAN/ANZ Use Cases

ANZ: Utilities and railway operators use ST GRID devices for workforce communication.
ASEAN: Oil & gas industry, metro projects (e.g., Malaysia’s MRT).

Pros and Cons

✅ Instant communication, hybrid cellular/PMR.
❌ Niche, not scalable for broadband IoT.

8.5 Satellite Systems

Beidou (China)

Global Navigation Satellite System (GNSS).
Provides positioning, navigation, timing (PNT).
Coverage: Global since 2020 (BDS-3).
ASEAN impact: integrated into agriculture and logistics.

Gouwang (China’s broadband LEO project)

LEO constellation under development.
Target: broadband satellite internet, competitor to Starlink.

Qianfan

Another Chinese LEO initiative for global internet coverage.
Focus on integration with Belt & Road countries, including ASEAN.

Starlink (SpaceX, USA)

LEO constellation (>5,000 satellites deployed as of 2024).
Low latency (~25–40 ms).
Broadband speeds 50–250 Mbps.
ASEAN: Pilots in Philippines, Malaysia.
ANZ: Widely used in rural Australia/New Zealand for broadband.

Inmarsat (UK)

Legacy GEO satellite operator.
Provides maritime, aviation, emergency communications.
Lower data rates, higher latency (~600 ms).

System	Orbit/Layer	Latency	Strengths	Limitations
Beidou	MEO/GEO	~50–100 ms	GNSS PNT	Not broadband
Gouwang/Qianfan	LEO (planned)	~25–40 ms	National broadband strategy	Still unproven
Starlink	LEO	25–40 ms	Broadband, rural coverage	Expensive, high CAPEX
Inmarsat	GEO	~600 ms	Aviation, maritime	High latency, limited throughput
LTE/5G	Terrestrial	1–20 ms	High capacity	Limited rural reach
LoRaWAN	Terrestrial ISM	Seconds	Ultra-low power IoT	Very low throughput
TETRA ATEX	PMR	<100 ms	Mission-critical voice	No broadband

8.6 Comparative Analysis: Satellite vs Terrestrial

AI/ML in Specialized Systems

LoRaWAN: ML-based anomaly detection in IoT sensor data.
TETRA ATEX: AI-based predictive maintenance of mission-critical radios.
Satellites:

Beam-hopping optimization using AI.
Traffic load prediction in LEO constellations.
AI-based GNSS error correction (multipath mitigation).

ASEAN and ANZ Regional Use Cases

ASEAN:

Singapore: LoRaWAN smart nation projects.
Philippines: Starlink trial for rural schools.
Indonesia: Beidou adoption in logistics/shipping.

ANZ:

Australia: Starlink integrated into Telstra partnerships.
New Zealand: LoRaWAN for agricultural IoT.
Mining/oil industries: ATEX walkie-talkies.

8.7 Summary

Specialized systems illustrate the breadth of wireless applications beyond mainstream 5G.

LoRaWAN dominates low-power IoT.
TETRA ATEX / ST GRID serve mission-critical industrial/public safety needs.
Satellites (Beidou, Starlink, Inmarsat) extend coverage where terrestrial fails.

Together, they form an ecosystem of complementary technologies. AI/ML ensures their integration into a seamless connectivity fabric for ASEAN and ANZ.

END CHAPTER 8

Chapter 9 - Core Networks: From MSC to 5GC with AI Enhancements

9.1 Introduction

While the Radio Access Network (RAN) defines how users connect to the network, the core network provides intelligence, switching, and control that enable mobility, authentication, and services. Core networks have evolved from circuit-switched telephony systems in 1G/2G to the all-IP, cloud-native, service-based architecture (SBA) of 5G.

This chapter explores core network evolution, with emphasis on:

MSC (Mobile Switching Centers) in 1G/2G.
SGSN/GGSN in 3G.
EPC (Evolved Packet Core) in LTE.
5GC (5G Core), cloud-native and AI-enabled.

We also highlight AI, data science, and ML integration in traffic engineering, security, and self-healing.

9.2 1G/2G Core Networks – Circuit Switching

Mobile Switching Center (MSC)

Centralized circuit switch.
Handles call setup, routing, and handovers.
Connected to PSTN for voice services.

Home/Visitor Location Registers (HLR/VLR)

HLR: Stores subscriber identity and authentication keys.
VLR: Temporary database for roaming subscribers.

Authentication Center (AuC)

Stores secret keys for SIM-based authentication.

Short Message Service Center (SMSC)

Enabled SMS delivery.

Limitations: No packet data, scalability issues, inflexible.

9.3 3G Core Networks – Dual Circuit and Packet

Serving GPRS Support Node (SGSN)

Routes data packets.
Tracks mobility for packet-switched services.

Gateway GPRS Support Node (GGSN)

Connects packet data traffic to external networks (e.g., internet).

Circuit/Packet Split

Dual domains coexisted: circuit (voice) + packet (data).

Impact: First step toward always-on data connections, though still limited compared to LTE.

9.4 4G Core Networks – The Evolved Packet Core (EPC)

EPC Architecture

MME (Mobility Management Entity): Controls mobility, authentication.
SGW (Serving Gateway): Routes user traffic.
PGW (Packet Gateway): Connects to external IP networks.
PCRF (Policy and Charging Rules Function): Manages QoS and charging

All-IP Architecture

Voice over LTE (VoLTE) through IMS (IP Multimedia Subsystem).
Circuit switching eliminated.

Benefits

Reduced latency.
Scalability for broadband.
Network sharing and virtualization enabled.

9.5G Core (5GC) – Service-Based Architecture

Service-Based Architecture (SBA)

Components exposed as services via APIs.
Cloud-native, microservices architecture.

Key Functions

AMF (Access and Mobility Management Function): Manages user access and mobility.
SMF (Session Management Function): Session setup, QoS policies.
UPF (User Plane Function): Handles user traffic forwarding.
NRF (Network Repository Function): Service discovery.
PCF (Policy Control Function): Policy management.
AUSF (Authentication Server Function): Authentication and security.

Features

Network Slicing: Logical partitioning for different services (e.g., URLLC vs eMBB).
Edge Computing (MEC): Applications hosted at the edge to reduce latency.
Non-Terrestrial Networks (NTN): Satellites integrated with 5GC.

9.6 Virtualization and Cloud-Native Cores

NFV (Network Function Virtualization): Replaces proprietary hardware with virtualized functions.
SDN (Software Defined Networking): Separates control/data planes, enabling programmability.
Containers and Kubernetes: Used in 5GC for cloud-native deployments.

9.7 AI and Data Science in Core Networks

7.7.1 Traffic Prediction and Optimization

ML models forecast traffic load, enabling dynamic scaling of UPFs.
Time-series models (ARIMA, LSTM) predict busy-hour congestion.

7.7.2 Network Slicing Management

AI orchestrators dynamically allocate slices.
Reinforcement learning optimizes slice performance.

7.7.3 Security and Anomaly Detection

ML-based intrusion detection identifies unusual traffic patterns.
AI-based fraud prevention (SIM cloning, DDoS mitigation).

7.7.4 Fault Management and Self-Healing

Predictive maintenance of virtual network functions (VNFs).
AI-based failure prediction in data centers.

9.8 ASEAN and ANZ Deployment Trends

ASEAN

Singapore: early adoption of 5GC standalone networks.
Malaysia: wholesale 5G model integrates 5GC for multiple operators.
Indonesia/Philippines: NSA rollouts dominate, limited standalone due to cost.

ANZ

Australia: Telstra deploying cloud-native 5GC with edge computing for industrial IoT.
New Zealand: Spark focusing on MEC for smart agriculture.

9.9 Summary

The core network has evolved from rigid circuit-switching (MSC) to flexible, AI-native service-based architectures (5GC).

1G/2G: voice-centric, circuit-switched.
3G: dual circuit + packet.
4G: all-IP EPC.
5G: service-based, virtualized, sliceable, AI-enabled.

Future 6G cores will be autonomous, cognitive, and self-optimizing, with AI not as an overlay but as a native capability.

END CHAPTER 9

Chapter 10 - Security and Authentication Across Generations

10.1 Introduction

Security in mobile networks has evolved alongside the technologies themselves. While 1G networks offered virtually no protection against eavesdropping and fraud, today’s 5G networks provide end-to-end encryption, mutual authentication, and subscriber privacy mechanisms.

However, each generation introduced new attack surfaces: cloning in 1G, weak ciphers in 2G, signaling storms in 3G, IMS vulnerabilities in 4G, and virtualized network security risks in 5G. Looking ahead, AI/ML-enhanced security frameworks are expected to underpin 6G.

10.2 1G: Insecure Beginnings

Analog FM voice: No encryption, easily intercepted by scanners.
Authentication: None; phone cloning rampant (copying ESN/IMSI).
Vulnerabilities:

Fraudulent calls billed to victims.
Eavesdropping in sensitive scenarios (e.g., law enforcement).

10.3 2G: Basic Digital Security

Authentication and Encryption

· SIM-based authentication: Introduced secret key (Ki) stored in SIM and AuC.

· A3/A8 algorithms: Generate session key (Kc).

· Ciphering: A5/1, A5/2 stream ciphers.

Weaknesses

· A5/1 eventually cracked with rainbow tables.

· No mutual authentication (network not authenticated by handset) → man-in-the-middle attacks.

10.4 3G: Stronger Mutual Authentication

UMTS Authentication and Key Agreement (AKA)

· Mutual authentication introduced.

· 128-bit keys.

· Stronger algorithms (Kasumi cipher).

Security Gains

· Protected against fake base stations.

· Improved integrity protection.

Limitations

· Still vulnerable to denial-of-service and jamming.

· Signaling overload attacks began to emerge.

10.5 4G: IP-Based Security Challenges

EPS-AKA

· Mutual authentication maintained.

· Session keys for encryption/integrity on IP bearer.

VoLTE and IMS Vulnerabilities

· SIP protocol in IMS exposed to spoofing, denial-of-service.

· Interworking with internet services increased attack surface.

LTE-Specific Threats

· IMSI Catchers (Stingrays): Exploited fallback to 2G/3G for interception.

· Rogue eNodeBs: Fake base stations used for surveillance.

10.6 5G: Enhanced Security and Privacy

5G Authentication and Key Agreement (5G-AKA)

· Based on EPS-AKA but enhanced.

· Introduced SUCI (Subscription Concealed Identifier) to protect IMSI.

· Home network authenticates user directly, reducing reliance on roaming operator.

Integrity Protection

· Extended to user plane as well as control plane.

Network Slicing Security

· Each slice may have independent security policies.

· AI used for adaptive policy enforcement.

Virtualization and Cloud Risks

· NFV/SDN components exposed to cloud vulnerabilities.

· Zero-trust architectures emerging.

10.7 AI and Data Science for Security

Anomaly Detection

· ML models trained on traffic patterns detect botnets, DDoS, or malware.

· Supervised and unsupervised learning for intrusion detection.

Threat Prediction

· Predictive analytics flag unusual signaling storms.

· Reinforcement learning enhances adaptive firewalls.

Privacy-Preserving AI

· Federated learning trains security models without centralizing sensitive data.

10.8 ASEAN and ANZ Security Considerations

ASEAN

· Some ASEAN countries still operate 2G → vulnerabilities remain.

· Increased risk from state and non-state actors exploiting outdated protocols.

· Singapore leading in 5G cybersecurity regulation.

ANZ

· Australia implemented 5G security bans on certain vendors citing supply chain concerns.

· New Zealand adopting cloud security frameworks for 5GC deployments.

10.9 Comparative Evolution of Security

Generation	Auth Method	Encryption	Weaknesses
1G	None	None	Cloning, eavesdropping
2G	SIM-based (A3/A8)	A5/1, A5/2	No mutual auth, weak ciphers
3G	AKA	Kasumi	DoS, jamming
4G	EPS-AKA	AES/Snow3G	IMS vulns, rogue eNodeB
5G	5G-AKA	256-bit AES, integrity	NFV/SDN risks, slice complexity

10.10 Summary

Security has advanced from non-existent (1G) to mutual, encrypted, privacy-preserving (5G). Yet, vulnerabilities evolve with technology: virtualized, cloud-native 5G cores create new risks. AI and ML are indispensable in threat detection, anomaly analysis, and adaptive security.

In ASEAN/ANZ, the coexistence of legacy 2G with cutting-edge 5G creates unique regional security challenges.

END CHAPTER 10

Chapter 11 – Performance, QoS, and KPI Engineering

11.1 Introduction

Performance engineering ensures that mobile and wireless networks meet user expectations and service-level agreements (SLAs).

This involves measuring Key Performance Indicators (KPIs), enforcing Quality of Service (QoS) classes, and optimizing resources to balance coverage, capacity, and cost.

With the advent of AI and big data analytics, performance management has shifted from reactive monitoring to predictive, self-optimizing systems.

11.2 Fundamental Performance Metrics

11.2.1 Coverage Metrics

RSRP (Reference Signal Received Power): Measures coverage strength.

RSRQ (Reference Signal Received Quality): Signal quality relative to interference.

SINR (Signal-to-Interference-plus-Noise Ratio): Key for throughput estimation.

11.2.2 Capacity Metrics

Cell throughput (DL/UL).

Spectral efficiency (bps/Hz).

Number of active users per cell.

11.2.3 Reliability Metrics

Block Error Rate (BLER).

Call Drop Rate.

Packet Loss.

11.2.4 Latency and Jitter

Critical for real-time applications (VoIP, gaming, URLLC).

11.3 QoS Frameworks

11.3.1 2G/3G Era

Best-effort packet data in GPRS.

Circuit-switched QoS for voice.

11.3.2 4G LTE

QoS Class Identifier (QCI): Defines priority, delay, packet loss.

Differentiates services (e.g., VoLTE vs video streaming).

11.3.3 5G NR

5QI (5G QoS Identifier): Flexible, service-specific.

Example:

URLLC: ultra-low latency, high reliability.
eMBB: high throughput.
mMTC: massive IoT connections.

11.4 KPI Engineering

9.4.1 Radio Access KPIs

Handover success rate.

RRC (Radio Resource Control) setup success.

PRB (Physical Resource Block) utilization.

11.4.2 Core Network KPIs

Session setup success rate.

Mean session duration.

Signaling load distribution.

11.4.3 Service KPIs

VoLTE MOS (Mean Opinion Score).

Video start time, buffering ratio.

IoT device availability.

11.5 AI and Data Science for Performance Optimization

11.5.1 Predictive Analytics

ML models (LSTM, ARIMA) forecast traffic loads.

Seasonal patterns detected in ASEAN/ANZ (e.g., urban rush hour vs rural IoT).

11.5.2 Root Cause Analysis

Data clustering isolates fault patterns.

AI distinguishes between hardware failure vs interference vs misconfiguration.

11.5.3 Self-Optimizing Networks (SON)

Dynamic PCI allocation.

Automatic load balancing.

AI-based antenna tilt and power optimization.

11.5.4 QoE (Quality of Experience) Prediction

Data fusion: combine KPI data with crowdsourced app-level metrics.

Example: Telstra’s ML-driven app experience scoring.

11.6 ASEAN and ANZ Deployment Context

11.6.1 ASEAN

Dense cities (Jakarta, Manila, Bangkok): KPI engineering focused on congestion and interference mitigation.

Smart city rollouts (Singapore): QoS tuned for IoT and AR/VR applications.

11.6.2 ANZ

Rural coverage: KPIs focused on coverage continuity and satellite integration.

Mining/industrial IoT: SLAs prioritize machine telemetry reliability over throughput.

11.7 Example KPI Targets (5G in ASEAN/ANZ)

KPI	Target (Urban)	Target (Rural)
DL throughput	1 Gbps+	100 Mbps+
Latency	<10 ms	<20 ms
Handover success	>99%	>97%
Call drop rate	<0.5%	<1%
IoT availability	99.9%	99%

11.8 Case Studies

11.8.1 Singapore

AI-enhanced KPI monitoring in 5G standalone networks.

Network slicing deployed for autonomous vehicle trials.

11.8.2 Australia

Telstra using AI to balance capacity between urban eMBB and rural fixed wireless access (FWA).

Integration with Starlink for outback connectivity.

11.9 Challenges

Data Overload: Billions of KPI data points daily → big data processing required.
Heterogeneity: LTE/5G/LoRaWAN coexistence complicates KPIs.
QoE Alignment: User experience often diverges from network-centric KPIs.
Security Risks: Exposing KPI data pipelines may create vulnerabilities.

11.10 Summary

Performance engineering evolved from basic voice KPIs in 2G to multi-layered AI-driven frameworks in 5G.

2G/3G: best-effort packet data.
4G: structured QoS via QCIs.
5G: ultra-flexible QoS via 5QIs, AI-driven KPI monitoring.

ASEAN and ANZ deployments highlight contrasting needs: urban congestion vs rural coverage. AI and ML are now indispensable for ensuring QoE, optimizing KPIs, and predicting failures.

END OF CHAPTER 11

Chapter 12 – Deployment, Optimization, and Testing

12.1 Introduction

Building a mobile or wireless system is not just about spectrum and standards; deployment and optimization are the decisive factors that determine whether a network meets expectations.

From site acquisition and radio planning to drive testing and AI-assisted optimization, network engineering requires a structured methodology. With 5G and beyond, automation and data-driven intelligence dominate, reducing reliance on manual fieldwork.

12.2 Deployment Fundamentals

12.2.1 Site Selection

Coverage-driven (rural ANZ): Maximize cell radius, fewer sites.

Capacity-driven (urban ASEAN): Densify with small cells and HetNets.

Consider zoning, environmental impact, and backhaul availability.

12.2.2 Spectrum Planning

Lower bands (<1 GHz): coverage.

Mid-bands (2–4 GHz): balance.

High bands (>24 GHz mmWave): hotspots.

12.2.3 Backhaul

Microwave links (cost-effective in ASEAN rural).

Fiber (preferred in cities like Sydney, Singapore).

Satellite integration for remote ANZ deployments.

12.3 Optimization Techniques

12.3.1 Radio Optimization

Antenna tilt and azimuth adjustments.

Power control: Reducing interference, extending battery life.

Carrier aggregation balancing.

12.3.2 Interference Management

ICIC/eICIC (Inter-Cell Interference Coordination).

Coordinated multipoint (CoMP).

Beamforming calibration in 5G.

12.3.3 Load Balancing

Cell reselection parameters tuned dynamically.

Traffic steering between LTE, 5G, and Wi-Fi.

12.4 Testing Methodologies

12.4.1 Drive Testing

· Traditional: engineers drive routes collecting RSRP, SINR, throughput.

· Tools: TEMS, Nemo, Rohde & Schwarz scanners.

· Challenge: costly, limited coverage.

12.4.2 Crowdsourced Testing

· App-based KPI collection (Ookla, OpenSignal).

· Offers broader coverage, lower cost.

· Privacy and accuracy remain issues.

12.4.3 Lab Testing

· Pre-deployment functional tests (conformance, stress, regression).

· Simulated RAN and core environments.

12.4.4 Field Trials

· Phased rollout in test markets.

· Regulatory and safety approval required.

12.5 AI and Data Science in Optimization

12.5.1 Automated Parameter Tuning

· AI systems self-adjust handover thresholds, antenna tilt, and power levels.

· Reinforcement learning optimizes beamforming in dense urban ASEAN networks.

12.5.2 Predictive Network Planning

· ML models forecast demand (e.g., holiday surges in Manila).

· Clustering algorithms identify underperforming cells.

12.5.3 Fault Detection

· AI detects anomalies in KPI streams.

· Self-healing RAN reroutes traffic automatically.

12.5.4 Testing Automation

· Digital twins simulate network rollout before deployment.

· AI accelerates regression testing of core functions.

12.6 ASEAN and ANZ Deployment Context

12.6.1 ASEAN

· Singapore: strong reliance on indoor small cells and HetNets.

· Indonesia: challenge of archipelagic geography → satellite backhaul + microwave.

· Vietnam/Thailand: government-driven 5G pilots with state-owned carriers.

12.6.2 ANZ

· Australia: Telstra and Optus focus on nationwide coverage, hybrid satellite integration.

· New Zealand: Spark targets agriculture IoT with precision deployment of LoRaWAN and LTE-M.

12.7 Comparative Testing Approaches

Method	Cost	Coverage	Accuracy	AI Integration
Drive Test	High	Limited	High	Low
Crowdsourced	Low	Wide	Moderate	High (data science pipelines)
Lab Testing	Medium	Simulated	High	High
Field Trial	Medium	Targeted	High	Medium

12.8 Challenges in Optimization

Heterogeneous networks (HetNets): Complicated interference patterns.
Multi-vendor environments: Interoperability issues.
Cost constraints: Especially in emerging ASEAN markets.
AI trustworthiness: Models must be explainable, not black-box.

12.9 Case Studies

12.9.1 Singapore 5G Standalone

· Heavy reliance on AI-automated optimization.

· Digital twin used for Marina Bay 5G rollout.

12.9.2 Australia Remote Connectivity

· AI-driven planning of satellite-cellular hybrid networks.

· Starlink integrated into outback schools with predictive traffic balancing.

12.10 Summary

Deployment and optimization evolved from manual field-driven engineering to AI-powered, data-driven automation.

2G/3G: manual drive testing and parameter tuning.
4G: semi-automated SONs.
5G: digital twins, AI-optimized RAN, predictive deployment.

In ASEAN, urban density and geographic fragmentation dominate deployment challenges. In ANZ, vast rural areas and industrial IoT drive reliance on automation and satellite integration.

END OF CHAPTER 12

Chapter 13 – Case Studies and Comparative Analysis

13.1 Introduction

Case studies illustrate how different mobile and wireless technologies have been applied in real-world contexts. Comparative analysis highlights strengths, weaknesses, and regional suitability across ASEAN and ANZ.

This chapter reviews use cases in cellular generations (2G–5G), Wi-Fi, LPWAN (LoRaWAN), mission-critical systems (TETRA ATEX, ST GRID), and satellite networks (Beidou, Starlink, Inmarsat, etc.).

13.2 Case Study 1 – GSM/2G in ASEAN (1990s–2000s)

Deployment

GSM widely deployed in ASEAN from 1993 onward.
900/1800 MHz bands.
Enabled SMS culture in Southeast Asia.

Impact

First affordable digital voice in rural and urban areas.
Led to economic transformation (mobile banking in Philippines).

Challenges

Weak encryption (A5/1 cracked).
Legacy persistence: 2G still active in parts of Myanmar, Cambodia.

13.3 Case Study 2 – UMTS/3G in ANZ (2005–2015)

Deployment

Telstra’s Next G network (850 MHz) in Australia was world-leading.
Extended 3G coverage across rural Australia.

Impact

Internet access in previously isolated communities.
Enabled agricultural IoT pilot projects.

Challenges

High CAPEX for nationwide deployment.
3G sunset (2024–2025) creates migration challenges.

13.4 Case Study 3 – 4G LTE in ASEAN Megacities

Deployment

Dense rollouts in Bangkok, Jakarta, Manila.
Spectrum used: 1800 MHz, 2100 MHz, 2600 MHz.

Impact

Mobile broadband penetration surged (>80% smartphone adoption).
Gig economy (Grab, Gojek) flourished with LTE connectivity.

Challenges

Congestion in dense urban environments.
Interference management critical.

13.5 Case Study 4 – 5G in Singapore and Australia

Deployment

Singapore: 5G standalone core with slicing (2020+).
Australia: Telstra and Optus launched 5G FWA (Fixed Wireless Access).

Impact

Singapore: 5G-enabled autonomous vehicles, smart ports.
Australia: rural households gain high-speed internet via 5G FWA + Starlink.

Challenges

ASEAN: patchy adoption, limited spectrum in some markets.
ANZ: mmWave rollout limited by cost and coverage.

13.6 Case Study 5 – Wi-Fi 6 in ASEAN Enterprises

Deployment

Wi-Fi 6 deployed in airports, universities, business parks.
Complementary to 5G indoor deployments.

Impact

Supports enterprise digitalization.
Singapore: hybrid Wi-Fi/5G private networks in Changi Airport.

Challenges

Congestion in 2.4 GHz band.
Limited indoor spectrum coordination with cellular.

13.7 Case Study 6 – LoRaWAN in New Zealand Agriculture

Deployment

LoRaWAN deployed for sheep/livestock monitoring, irrigation.
Coverage: 10–15 km from base stations.

Impact

Improved productivity and water efficiency.
Reduced OPEX due to battery life (10 years+).

Challenges

Low data rates (not suitable for multimedia telemetry).
Dependence on unlicensed spectrum (interference risk).

13.8 Case Study 7 – TETRA ATEX in ASEAN Oil & Gas

Deployment

ATEX-certified TETRA radios used on offshore rigs in Brunei, Malaysia.
Mission-critical communication in hazardous zones.

Impact

Safety compliance in flammable environments.
Robust voice communications under extreme conditions.

Challenges

Not suitable for data-heavy IoT.
Proprietary devices with high cost.

13.9 Case Study 8 – Starlink in Rural ANZ

Deployment

Starlink terminals installed in Australian Outback schools and New Zealand farms.
Latency ~30–40 ms, throughput 50–200 Mbps.

Impact

Bridged digital divide in rural education.
Enabled telehealth and online education.

Challenges

Cost of terminals (~USD 600) limits adoption.
Weather sensitivity (rain fade).

11.10 Comparative Technology Table

Technology	Latency	Throughput	Strengths	Limitations	ASEAN/ANZ Use Case
GSM/EDGE	150+ ms	~0.2 Mbps	Coverage, maturity	Low data	SMS-driven culture in ASEAN
UMTS/HSPA	50–100 ms	~14–42 Mbps	Proven, flexible	Interference	Telstra Next G rural coverage
LTE	20–50 ms	100s Mbps–Gbps	Ecosystem, efficiency	Dense planning	ASEAN megacities broadband
5G NR	<10 ms	>1 Gbps	Low latency, slicing	Complexity	Singapore smart nation
Wi-Fi 6	<10 ms	Multi-Gbps	Cost-effective	Interference	Airports, enterprises
LoRaWAN	Seconds	kbps	Long range, IoT	Low throughput	NZ agriculture
TETRA ATEX	~100 ms	Narrowband	Mission-critical	Not broadband	Oil & gas ASEAN rigs
Starlink	30–40 ms	50–200 Mbps	Rural coverage	Cost, weather	ANZ schools & farms

13.11 AI/ML in Case Study Applications

ASEAN megacities (LTE/5G): AI-driven interference coordination.
ANZ rural Starlink: AI optimizes satellite beam allocation.
LoRaWAN NZ farms: ML models predict soil irrigation schedules.
Singapore 5G slicing: Reinforcement learning allocates slices dynamically.

13.12 Summary

Case studies demonstrate the complementary role of mobile, Wi-Fi, LPWAN, mission-critical, and satellite systems:

2G/3G laid foundations but persist as vulnerabilities.
4G/5G dominate urban ASEAN/ANZ broadband.
Wi-Fi 6 complements indoor capacity.
LoRaWAN thrives in agriculture IoT.
TETRA ATEX ensures mission-critical safety.
Starlink bridges rural broadband gaps.

AI/ML consistently enhances performance, reliability, and security across all case studies.

END OF CHAPTER 13

Chapter 15 – 5G Advanced and the Road to 6G

15.1 Introduction

While 5G networks are still being deployed globally, research and standardization have already shifted toward 5G Advanced (3GPP Release 18 and beyond) and 6G (expected 2030).

The focus is on making networks:

AI-native (intelligence embedded into the protocol stack).
Sustainable (green networking, energy efficiency).
Ubiquitous (integrated terrestrial, aerial, and satellite).
Convergent (communications, sensing, and computing combined).

15.2 5G Advanced (Release 18–20)

15.2.1 Key Enhancements

· Extended MIMO: Extremely large antenna arrays with AI beam management.

· Positioning Accuracy: Sub-meter precision for industrial IoT and autonomous vehicles.

· XR Support: Optimized for AR/VR with latency <5 ms.

· Uplink-Centric Design: Prioritizing sensor-heavy IoT and metaverse devices.

· Non-Terrestrial Networks (NTN): Native satellite integration (LEO, GEO, HAPS).

15.2.2 AI-Native RAN

· AI models trained to optimize scheduling, resource allocation, and interference coordination.

· Network functions exposed as APIs for real-time ML training.

15.2.3 ASEAN/ANZ Applications

· ASEAN: Smart factories in Malaysia, Thailand → low-latency positioning.

· ANZ: Agriculture robotics with uplink-heavy IoT, powered by AI-optimized 5G.

15.3 Toward 6G (IMT-2030)

15.3.1 Target Capabilities

· Data Rates: Up to 1 Tbps peak.

· Latency: Sub-millisecond (0.1–0.5 ms).

· Sensing: Integrated radar-like capabilities in radio beams.

· AI-Native Architecture: AI/ML embedded at protocol design, not as an add-on.

· Sustainability: Net-zero carbon networks.

15.3.2 Frequency Ranges

· Sub-THz (100–300 GHz): Provides extreme bandwidth but limited range.

· Visible Light Communication (VLC): Integration with optical systems.

15.3.3 Key Enablers

· Reconfigurable Intelligent Surfaces (RIS): Smart reflective panels steering signals dynamically.

· Extreme MIMO: Thousands of antenna elements.

· Joint Communication and Sensing (JCAS): Networks double as radar/sensors.

· Quantum-Safe Security: Resistant to post-quantum attacks.

15.4 AI and Data Science in 6G

AI-Centric Design: Scheduling, waveform selection, and topology self-optimized.
Digital Twins: Entire networks simulated in real-time for predictive optimization.
Federated Learning: Privacy-preserving AI training across devices.
Self-Evolving Networks: Reinforcement learning adapts protocols on the fly.

15.5 Non-Terrestrial Networks (NTN)

15.5.1 Satellite Integration

· 5G Advanced → 6G: Direct-to-device satellite service.

· LEO constellations integrated with cellular core networks.

15.5.2 ASEAN/ANZ Relevance

· ASEAN: Disaster recovery networks after typhoons/earthquakes.

· ANZ: Outback IoT (mining, agriculture) seamlessly using NTN.

15.6 6G Use Cases

15.6.1 Industrial & Autonomous Systems

· ASEAN: Autonomous ports in Singapore, Malaysia.

· ANZ: Robotic farming with AI-powered real-time sensing.

15.6.2 Extended Reality (XR) & Metaverse

· Holographic communication with Tbps uplink/downlink.

· Seamless global presence for work and entertainment.

15.6.3 Smart Cities & Environmental Monitoring

· ASEAN megacities: Real-time air pollution sensing + traffic management.

· ANZ: Wildfire detection via UAV-6G integrated systems.

15.7 Comparative Timeline

Generation	Peak Rate	Latency	Key Innovations
4G LTE	1 Gbps	20–50 ms	All-IP, MIMO
5G NR	10 Gbps	<1 ms	Slicing, massive MIMO, NTN
5G Advanced	~20 Gbps	<0.5 ms	AI-native RAN, XR, precise positioning
6G	1 Tbps	0.1–0.5 ms	RIS, JCAS, sub-THz, AI-centric

15.8 Challenges

Hardware Constraints: Sub-THz requires new semiconductor materials.
Energy Consumption: Ultra-dense antennas must be energy-efficient.
Standardization: Global consensus on IMT-2030 framework.
Security: Quantum-resistant cryptography needed.

15.9 ASEAN and ANZ Roadmaps

15.9.1 ASEAN

· Singapore, Thailand, Malaysia actively participating in ITU 6G studies.

· Focus on smart cities, industrial IoT.

15.9.2 ANZ

· Australia and New Zealand prioritizing agriculture, mining, and remote connectivity.

· Early adoption of NTN for vast territories.

15.10 Summary

5G Advanced strengthens current 5G with AI-native features, uplink optimization, and satellite integration.

6G envisions AI as the network itself: Tbps speeds, near-zero latency, sensing + communications convergence.

ASEAN and ANZ will apply these differently:

· ASEAN → dense urban smart cities, Industry 4.0.

· ANZ → agriculture, mining, remote IoT with satellite integration.

END OF CHAPTER 15

Chapter 17 – Conclusion

17.1 Overview

This thesis traced the technical evolution of mobile and wireless systems from the analog 1G era of the 1980s to 5G and the trajectory toward 6G, while also exploring parallel ecosystems such as Wi-Fi, LoRaWAN, TETRA ATEX, ST GRID devices, and satellite networks (Beidou, Starlink, Inmarsat, Gouwang, Qianfan).

Across ASEAN and ANZ, the comparative analysis demonstrated that technology adoption reflects regional socio-economic and geographic realities:

ASEAN megacities → dense LTE/5G rollouts, smart city integration.
ANZ rural/remote regions → satellite and hybrid systems critical for inclusion.

17.2 Key Findings

17.2.1 Generational Progression

1G: Introduced cellular concept, but no security.
2G: Digitalization, SMS culture, basic security.
3G: Mobile broadband foundation, yet uneven global adoption.
4G LTE: All-IP, high capacity, foundation for the gig economy.
5G NR: AI-ready, ultra-low latency, slicing, satellite integration.

17.2.2 Specialized Systems

LoRaWAN: Ideal for long-range IoT, but limited throughput.
TETRA ATEX / ST GRID: Mission-critical, ruggedized comms for safety industries.
Satellite Systems: Complement terrestrial, crucial for rural ANZ and disaster-stricken ASEAN.

17.2.3 AI and Data Science

Now central to:

Ø Traffic prediction.

Ø Security anomaly detection.

Ø Automated optimization (SON, digital twins).

Ø QoS and QoE assurance.

17.3 Regional Insights

ASEAN

Diversity in deployment maturity: Singapore (5G advanced), Cambodia/Myanmar (legacy 2G still active).
Smart city initiatives driving IoT adoption.
Geographical fragmentation (archipelagos like Indonesia, Philippines) → satellite + terrestrial hybrids.

ANZ

Vast landmass and low population density → heavy reliance on macrocells + satellite.
Strong integration of LoRaWAN and LTE-M in agriculture.
Starlink + Telstra partnerships extend digital equity in Australia’s Outback.

17.4 Future Outlook

17.4.1 5G Advanced

Ø Strengthens positioning, uplink, XR, and NTN.

Ø AI becomes intrinsic to RAN/core functions.

17.4.2 6G Vision

Ø 1 Tbps peak rates, 0.1 ms latency.

Ø Integrated communication + sensing.

Ø Reconfigurable Intelligent Surfaces (RIS), sub-THz spectrum.

Ø Quantum-safe, AI-native networks.

ASEAN will leverage 6G for urban industrial automation, while ANZ will apply it to agriculture, mining, and remote connectivity.

17.5 Research Methodology Reflection

Historical review: Standards evolution analyzed from 3GPP, ITU, IEEE documentation.
Comparative analysis: Technologies benchmarked in terms of latency, throughput, security, and use cases.
Case studies: ASEAN/ANZ deployments studied from operator whitepapers, government policies, and academic research.
AI/ML integration: Evaluated through contemporary research on SONs, digital twins, and anomaly detection.

17.6 Final Thoughts

The central theme is convergence:

Cellular, Wi-Fi, LPWAN, and satellite are not competing but complementary.
AI/ML ensures networks become adaptive, autonomous, and predictive.
Future 6G will be a cognitive fabric — sensing, computing, and communicating seamlessly.

The evolution from 1G to 5G and beyond is not merely technological but also socio-economic, shaping digital inclusion in ASEAN and ANZ. The challenge for researchers and policymakers is ensuring that technological sophistication aligns with accessibility, affordability, and security.

REFERENCES & BIBIOGRAPHY

[1]

Canonical: 3GPP TS 23.501, "System Architecture for the 5G System (5GS)"

URL: https://www.3gpp.org/ftp/Specs/archive/23_series/23.501/

Used In Thesis Section(s): Ch.8 Core Networks (MSC→EPC→5GC), Ch.6 Standards (5G), Ch.13 5G-Advanced

Referenced Statement: 5GC is a cloud-native Service-Based Architecture (SBA) with functions such as AMF, SMF, UPF, NRF, PCF communicating via service APIs.

[2]

Canonical: 3GPP TS 38.300, "NR; NR and NG‑RAN overall description"

URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.300/

Used In Thesis Section(s): Ch.6 Standards (5G NR), Ch.8 Core Networks (RAN ↔ Core interfaces)

Referenced Statement: Overall description of 5G NR/NG‑RAN, including architecture, protocol layers, and reference signals.

[3]

Canonical: 3GPP TS 36.300, "E‑UTRA and E‑UTRAN; Overall description; Stage 2"

URL: https://www.3gpp.org/ftp/Specs/archive/36_series/36.300/

Used In Thesis Section(s): Ch.5 Topologies (macro/micro/pico), Ch.6 Standards (LTE), Ch.4 Modulation/Access

Referenced Statement: LTE uses OFDMA (downlink) and SC‑FDMA (uplink) and defines key E‑UTRAN procedures.

[4]

Canonical: ETSI GSM 05.05 (3GPP TS 45.005), "Radio transmission and reception"

URL: https://www.etsi.org/deliver/etsi_gts/05/0505/05.05.01_60/ | https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=1448

Used In Thesis Section(s): Ch.2 Timeline (2G), Ch.6 Standards (GSM)

Referenced Statement: GSM carrier spacing is 200 kHz; baseline RF characteristics for GSM.

[5]

Canonical: 3GPP HSPA (Release 5/6) — see TS 25.308 / TS 25.306; summary: "High Speed Packet Access (HSDPA/HSUPA)"

URL: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=1411

Used In Thesis Section(s): Ch.2 Timeline (3G), Ch.6 Standards (UMTS/HSPA)

Referenced Statement: HSDPA introduced downlink peak rates on the order of ~14.4 Mb/s with link adaptation and fast scheduling.

[6]

Canonical: 3GPP TR 38.901, "Study on channel model for frequencies from 0.5 to 100 GHz"

URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.901/

Used In Thesis Section(s): Ch.3 Propagation & Link Budget, Ch.6 Standards (5G NR), Ch.13 5G‑Advanced

Referenced Statement: NR channel modeling across sub‑6 GHz and mmWave, including path loss, delay spread, and mobility (Doppler) effects.

[7]

Canonical: ITU‑R P.525‑4, "Calculation of free-space attenuation"

URL: https://www.itu.int/rec/R-REC-P.525/en

Used In Thesis Section(s): Ch.3 Propagation & Link Budget

Referenced Statement: Free‑space path loss (FSPL) formula used as baseline for link budgets.

[8]

Canonical: 3GPP TS 33.501, "Security architecture and procedures for 5G System"

URL: https://www.3gpp.org/ftp/Specs/archive/33_series/33.501/

Used In Thesis Section(s): Ch.9 Security & Authentication, Ch.6 Standards (5G), Ch.8 Core Networks

Referenced Statement: 5G‑AKA, SUCI (concealed SUPI), and mapping of integrity/confidentiality algorithms (e.g., NEA1/2/3, NIA1/2/3).

[9]

Canonical: NIST Cybersecurity White Paper — SUCI Overview (supporting 5G privacy)

URL: https://csrc.nist.gov/publications

Used In Thesis Section(s): Ch.9 Security & Authentication

Referenced Statement: Rationale and mechanism for protecting subscriber identifiers via SUCI to mitigate IMSI‑catcher attacks.

[10]

Canonical: LoRa Alliance, "LoRaWAN® L2 1.0.4 Specification"

URL: https://lora-alliance.org/resource_hub/lorawan-specification-1-0-4/ | https://resources.lora-alliance.org/

Used In Thesis Section(s): Ch.7 Specialized Systems (LoRaWAN), Ch.6 Standards (LPWAN)

Referenced Statement: Device classes (A/B/C), MAC behavior, and certification references for LPWAN deployments.

[11]

Canonical: IEEE 802.11ax (Wi‑Fi 6) standard — overview via vendor technical white papers

URL: https://www.cisco.com/c/en/us/solutions/collateral/enterprise-networks/802-11ax-solution/white-paper-c11-740788.html

Used In Thesis Section(s): Ch.6 Standards (Wi‑Fi), Ch.10 Performance/QoS (dense deployments)

Referenced Statement: OFDMA, BSS Coloring, and Target Wake Time (TWT) as key Wi‑Fi 6 features for efficiency in dense environments.

[12]

Canonical: LANCOM Systems Whitepaper (Wi‑Fi 6)

URL: https://www.lancom-systems.com/wi-fi6-whitepaper/

Used In Thesis Section(s): Ch.6 Standards (Wi‑Fi)

Referenced Statement: Practical explanations of 802.11ax mechanisms (OFDMA, BSS Coloring) and deployment guidance.

[13]

Canonical: IEEE 802.16e‑2005 (Mobile WiMAX) — mobility/QoS extensions

URL: https://standards.ieee.org/standard/802_16e-2005.html | https://ws680.nist.gov/publication/get_pdf.cfm?pub_id=150568

Used In Thesis Section(s): Ch.6 Standards (WiMAX), Ch.2 Timeline (4G contenders)

Referenced Statement: Mobile WiMAX extensions enabling mobility, QoS, and PHY/MAC enhancements; historical 4G context.

[14]

Canonical: 3GPP TR 38.821, "Solutions for NR to support non‑terrestrial networks (NTN)"

URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.821/

Used In Thesis Section(s): Ch.6 Standards (5G/NTN), Ch.7 Specialized Systems (Satellites), Ch.13 Road to 6G

Referenced Statement: Architectural and PHY/MAC adaptations for NR over LEO/HAPS, including timing/Doppler considerations.

[15]

Canonical: 3GPP TS 38.211/38.213/38.214 (NR PHY, control, and procedures) — numerology

URL: https://www.3gpp.org/ftp/Specs/archive/38_series/38.211/

Used In Thesis Section(s): Ch.4 Modulation/Coding/Multiple Access, Ch.6 Standards (5G NR)

Referenced Statement: NR subcarrier spacing Δf = 15×2^μ kHz, slot/frame structure, MCS tables and link adaptation context.

[16]

Canonical: ETSI/3GPP Algorithm specifications (Snow3G, AES‑CTR, ZUC) — mapped via TS 33.501 Annexes

URL: https://www.3gpp.org/ftp/Specs/archive/35_series/ | https://www.3gpp.org/ftp/Specs/archive/33_series/33.501/

Used In Thesis Section(s): Ch.9 Security & Authentication

Referenced Statement: Families of confidentiality (NEA1/2/3) and integrity (NIA1/2/3) algorithms used in LTE/NR.

[17]

Canonical: ITU‑R P.838‑3 (specific attenuation model for rain) and P.840‑8 (rain height/cloud models)

URL: https://www.itu.int/rec/R-REC-P.838/en | https://www.itu.int/rec/R-REC-P.840/en

Used In Thesis Section(s): Ch.3 Propagation (mmWave rain fade), Ch.13 5G‑Advanced (FR2 planning)

Referenced Statement: Quantitative rain attenuation models applicable to FR2/mmWave planning.

[18]

Canonical: RF Cafe (historical engineering tables) — AMPS 30 kHz channelization

URL: https://www.rfcafe.com/references/electrical/wireless-communications-standards.htm

Used In Thesis Section(s): Ch.2 Timeline (1G), Ch.6 Standards (1G)

Referenced Statement: AMPS channel spacing of 30 kHz and associated band plans (historical reference).

[19]

Canonical: 3GPP TS 23.401 (EPC); IMS/VoLTE (TS 24.229); 24/26 series for SIP/codec

URL: https://www.3gpp.org/ftp/Specs/archive/23_series/23.401/ | https://www.3gpp.org/ftp/Specs/archive/24_series/24.229/ | https://www.3gpp.org/ftp/Specs/archive/26_series/

Used In Thesis Section(s): Ch.8 Core Networks (LTE/EPC), Ch.10 Performance/QoS

Referenced Statement: EPC split (MME, SGW, PGW) and IMS/VoLTE SIP-call control with codec implications.

[20]

Canonical: 3GPP TS 36.212/36.213 (LTE PHY coding & modulation; link adaptation)

URL: https://www.3gpp.org/ftp/Specs/archive/36_series/

Used In Thesis Section(s): Ch.4 Modulation/Coding (4G), Ch.10 Performance/QoS

Referenced Statement: Coding/modulation schemes, HARQ, higher-order QAM, and MCS tables used for LTE/LTE‑A throughput claims.

[21]

Canonical: 3GPP TS 45.002/45.003 (GSM physical channel, modulation)

URL: https://www.3gpp.org/ftp/Specs/archive/45_series/

Used In Thesis Section(s): Ch.4 Modulation/Coding (2G), Ch.2 Timeline (2G)

Referenced Statement: GMSK modulation, time-slot/multiframe structure for GSM; groundwork for GPRS/EDGE evolution.

[22]

Canonical: 3GPP TS 25.331/25.211 (UMTS/W‑CDMA RRC & PHY)

URL: https://www.3gpp.org/ftp/Specs/archive/25_series/

Used In Thesis Section(s): Ch.4 Modulation/Coding (3G), Ch.2 Timeline (3G)

Referenced Statement: W‑CDMA spreading/channelization and RRC procedures; HSPA context.

[23]

Canonical: IEEE 802.11be (Wi‑Fi 7) – overview (IEEE/vendor)

URL: https://standards.ieee.org/ieee/802.11be/7421/ | https://www.intel.com/content/www/us/en/products/docs/wireless/wi-fi-7.html

Used In Thesis Section(s): Ch.6 Standards (Wi‑Fi evolution), Ch.10 Performance/QoS

Referenced Statement: 320‑MHz channels and Multi‑Link Operation (MLO) as Wi‑Fi 7 advancements.

[24]

Canonical: LoRa Alliance — Regional Parameters (RP2‑1.0.4)

URL: https://lora-alliance.org/resource_hub/rp2-1-0-4-lorawan-regional-parameters/

Used In Thesis Section(s): Ch.7 Specialized Systems (LoRaWAN), Ch.3 Spectrum (ISM bands)

Referenced Statement: Regional channel plans, duty‑cycle, and EIRP constraints for LoRaWAN.

[25]

Canonical: ITU IMT documents (IMT‑Advanced & IMT‑2020 requirements/definitions)

URL: https://www.itu.int/en/ITU-R/terrestrial/im/pages/im.aspx

Used In Thesis Section(s): Ch.2 Timeline (4G/5G definitions), Ch.6 Standards

Referenced Statement: Formal generational requirements and performance frameworks for IMT‑Advanced (4G) and IMT‑2020 (5G).

Image Credits

Credits to image owners under Creative Commons or taken in full context

Science Museum Group. Motorola StarTAC mobile phone, 1997.. 1997-1650 Science Museum Group Collection Online. Accessed 4 October 2025.

https://collection.sciencemuseumgroup.org.uk/objects/co430373/motorola-startac-mobile-phone-1997.

Science Museum Group. Motorola 4800 Analogue Mobile. Y2005.44.1 Science Museum Group Collection Online. Accessed 5 October 2025. https://collection.sciencemuseumgroup.org.uk/objects/co8413432/motorola-4800-analogue-mobile.

Science Museum Group. Motorola A1000 mobile phone. 2014-495/1 Science Museum Group Collection Online. Accessed 5 October 2025. https://collection.sciencemuseumgroup.org.uk/objects/co8442766/motorola-a1000-mobile-phone

Science Museum Group. Nokia 9000 Communicator mobile phone. 2014-44 Science Museum Group Collection Online. Accessed 5 October 2025. https://collection.sciencemuseumgroup.org.uk/objects/co8439032/nokia-9000-communicator-mobile-phone

Credit to SAMSUNG (Galaxy A12, taken out from a free-to-distribute)

POST GRADUATE PATHWAY

KEY TAKE AWAY IMAGES

Abstract & Intro

Timeline overview (1980s–5G).
Conceptual diagram: radio systems ecosystem (cellular, Wi-Fi, LPWAN, satellite).
World map showing ASEAN & ANZ focus.

Chapter 1 – Introduction

Cellular architecture basics.
Comparative chart: wireless vs wired.
Illustration of spectrum evolution.

Chapter 2 – Historical Timeline

1G–5G evolution timeline.
Generational throughput/latency chart.
Device evolution pictorial (brick phone → smartphone → IoT).

Chapter 3 – Spectrum & Regulation

900 MHz propagation graph.
ASEAN spectrum allocation table (visualized).
ANZ spectrum licensing diagram.

Chapter 4 – Propagation & Link Budget

Path loss models (free space vs Hata).
Link budget block diagram.
Antenna/MIMO illustration.

Chapter 5 – Network Topologies

Macro/micro/pico/femto cell hierarchy.
HetNet diagram.
Mesh/LPWAN star-of-stars.

Chapter 6 – Standards by Generation

Table: 1G–5G standards comparison.
Wi-Fi evolution chart.
LoRaWAN architecture.

Chapter 7 – Specialized Systems

TETRA ATEX rugged radio pictorial.
ST GRID hybrid phone diagram.
Satellite constellations map (Beidou, Starlink, Inmarsat).

Chapter 8 – Core Networks

MSC → EPC → 5GC evolution.
Service-based architecture diagram.
NFV/SDN cloud-native illustration.

Chapter 9 – Security & Authentication

Authentication methods evolution (1G–5G).
Attack surface diagram (IMSI catcher, rogue base station).
AI-based intrusion detection pipeline.

Chapter 10 – Performance & QoS

KPI dashboard mockup.
QCI/5QI mapping table.
Throughput/latency scatterplot.

Chapter 11 – Deployment & Testing

Drive test vs crowdsourced testing diagram.
Digital twin illustration.
Optimization workflow (AI loop).

Chapter 12 – Case Studies

ASEAN adoption map.
ANZ rural broadband coverage.
Comparative chart: LTE vs Starlink vs LoRaWAN.

Chapter 13 – 5G Advanced & 6G

RIS (reconfigurable intelligent surfaces) pictorial.
Sub-THz spectrum chart.
6G vision roadmap (timeline).

Chapter 14 – Conclusion

Pyramid diagram (1G foundation → 6G cognitive fabric).
ASEAN vs ANZ summary chart.
Future integration flowchart (AI + satellite + terrestrial).

The SCOT have arrived

2025-02-27T19:06:00.000-08:00

Finally after more than 15 years, the Scotcoin project finally peaked and went into listing with MEXC Exchange last week, where it started off with SCOTUSDT par and floated at 3USDT till it peaked at 25USDT.

For many, they are over the moon on this news till I did a little more digging: They were on another smart contract at 0x28ed4FD6DeDB226B16b92CE6997f7CdfDf0c519B

So the new contract is in a new place, where they burned 200:1 ratio so the coins are at $1 instead of 5c at float., hence there is about 250Million SCOT which works to USD250Million

Anyway, the MEXC do not accept Singapore, UK, US customers...so use Malaysian or some other one k?

Financial Disclaimer

2025-02-19T01:47:00.000-08:00

So I have been LEGALLY ADVISED to put this as part of the OFFICIAL DOCUMENTS as I am both a Singapore Citizen, Malaysian Tax Resident and a UK Legal Tax Resident as I have a valid UK License.

Mainly because of Trump, DOGE is on my tail. Yes..Mr Musk has to tell BitCoiners, MVL Chainers and Block Chain hard core HODL-ers alike.

And ooh Ya... SCOTCOIN is now live on MECN. And I cannot put my referral link ..else I get a smack again [as Fortinet classfied me as Financial Entities]

Disclaimer:

This document is for information only and for general circulation. It (or any part of it) shall not form the basis of, or be relied on in connection with, any contract or commitment. It should not be construed as a distribution, an offer to sell, a solicitation of an offer to purchase, a recommendation to buy or sell, or a forecast on future performance of, any investment or securities in any jurisdiction where such activities would be unlawful under the laws of such jurisdiction, such as the United States and Canada.

The specific investment objectives, personal situation and particular needs of any person have not been taken in consideration. You should not rely on this document as investment advice. In particular, if you have limited knowledge on investment products, you are encouraged to seek advice from a financial adviser. If you choose not to seek advice from a financial adviser, you should carefully consider whether the investment is suitable. Any transaction that you decide to make will be of your own choice and at your own risk.

The value of investments, units in the funds and the income accruing to the units, if any, may fall or rise, and the investor may not get back the original sum invested. Past performance is not necessarily indicative of future performance. Investors and potential investors should read the relevant prospectus, offering document or product information before investing.

Umrah DIY Guide

2024-09-30T17:40:00.000-07:00

Some of our readers have been asking me how to do a cheap DIY UMRAH or Mini Hajj Pilgrimage to Mecca / Makkah in Saudi Arabia as the country now have relaxed the rules.

Some key notes that you should know: WEATHER, SEASON, BLACKOUT DATES, ROUTE.

Weather : Saudi weather is TYPICAL ARABIAN and meaning it is on the extremes. Hot weather summer can reach up to 60°C or so in Summer. Last Hajj season, a few hundred pilgrims passed away due to heat stroke as weather is scorching hot. Best avoid between May-Sept

Cooler weather starts October to Dec and Wet and chilly between Dec-Feb as heavy rainfall occurs. Trust me... it's like chilled ice water falling on you. A nice touch especially when you do the tawaf in the mataf and people will try avoid it..and thats when U can go and kiss the Kaabah and Hajar Aswad.

Blackout dates : Your umrah visa IS NOT VALID DURING HAJJ SEASON. Dhulkaedah and Dhulhijjah (After Syawal 2 months) as the Hajj Season has begin. This year, its WHOLE OF May & June 2025. Many will do Haji Irfad.And recent years, arrests and deportations are rampant and the police will break into the hotel rooms to catch violators.

ROUTE : If you are flying in for just UMRAH, then your route should be direct into Jeddah (JED) Airport with a simple stop-over. I strongly suggest take QATAR AIRWAYS (I don't get comm ok) but their service is really good. Emirates is great too but U will tend to waste time and effort indulging in shopping.

If you wish to do a ziarah *(VISITING Rasulullah ) , you need to do a MULTI-CITY and very likely it will require a 12-48hr stop over. I find it cheaper to Stop over in Doha and carry on. But you will require to take your luggage out.

This is an important step as you need to book your HARAMAIN TRAIN TICKETS proper. Last minutes will cost you dearly.

Book on a TUESDAY as it was said to be cheaper. You can verify with Flights Google
Example is SIN-DOH DOH-MED JED-SIN is cheaper than SIN-MED JED-SIN. Note: These are assuming you are flying from Singapore. I do notice flights from KUL and CGK are far expensive as the route is more expensive.

HOTEL :
CHEAP DOES NOT MEAN GOOD.
EXPENSIVE DOES NOT MEAN VERY CONVINIENT.

I learn it the hard way. I booked at the ZAMZAM CLOCK TOWER. Building is abit of old shared with Swisotel, Fairmont, and many more, so U need to take the lift from the mall to P11 or P5 or P3 depending on your hotel to check in. And whats worse, is that UBER CANNOT GO THERE. Only TAXI allowed into the Carpark driveway and they are a bloody ripoff. Even with meter, they refuse to do so, and charge anywhere from SAR20-SAR100 from HARAMAIN TRAIN STATION.

If you are unlucky, they will throw you at the underpass of Aziziyyah road..and you need to drag your bags up all the way!

Hence I recommend strongly AL-EBAA hotel , just after ANJUM hotel as it is outside the UBER CORDON AREA. ANJUM is great hotel, full of Indonesians but the climb up the hill of Jabal Omar (JABAL means HILL) is a journey in itself as it is located off the 3rd Extension project, 900m to the MATAF. Btw, Mataf means the tawaf area where the Kaabah is.

Price wise for Al-Ebaa is about S$100 for a 3 bed room.

Price comparision for CNY season.

Ramadan 2025 price is really crazy!

I will continue in a second part

My QR for Malaysian

2024-05-30T20:10:00.000-07:00

MyTrip the equivalent of Singapore QR code for immigration clearance is here.

How to do?

2 things needed: MySejahtera and MyDigitalID.

Yes.. MY SEJAHTERA...That stupid Covid app thing.

Its quite simple to link the KYC.. IC front n back and selfie...and it works...then login to mysejahtera using the MyDigitalID. Macam SingPass lah.

Then go to MySejahtera ... Click on MyTrip.. and link passport. You need to scan the MRZ... take the foto long way...

Then once verified.. the MyTRIP QR shows this...

Good luck

Digital Nomad in APAC

2023-09-27T00:50:00.003-07:00

Some people find it being a digital nomad is the best option to work. Since Covid-19 Pandemic, remote working, aka WFH is the next best thing for work life. But there are some downside if you just WFH and lack the social skills.

So thus, many opt for the Digital Nomad hot desking at remote locations worldwide. Me too, despite based in London, I am happy in Sunny Singapore, with services and facilities available 24/7 for the most time of it.

Getting to work is one thing, but making sure you comply with the local law is another. Despite many people loving Singapore, they have a strict law regarding immigration as many thought they can do the normal "Visa Runs". Even Singaporeans sometimes run foul of the laws in Malaysia by living there continuously or even daily commute.

Malaysia: They have a new program called DE Rantau by MDEC , which is the Malaysian Digital Economy Corporation. However, you may be subjected to Malaysian tax regimes which as a foreigner, is as high as 28% since the Ringgit is very weak *(RM4.70-USD/RM3.40-SGD) and with average pay in Singapore for newly graduate IT Tech Support L1 is about USD29K a year, the tax would be about RM11,000 or USD 2500 *(roughly 10%) vs people like me, will be paying about 20% in Income Tax.

In a professional view, a Junior IT Engineer, will be earning S$5000+ *(S$60,000++pa) have to pay about RM 55,000 *(SG$15,000) in Malaysian tax compared to about S$2,700 in Singapore alone. Assumption is Single without any deductibles available.

Cost in Malaysia is very cheap! RM1500 in JB will get a good guarded condominium with gym etc whereas average like-for-like in Singapore will set you off for $4500 *(RM15k)

But the red tapes...oh my lord...it's never an easy thing. Singapore is digitised. But..they do not really welcome nomads. You need a working permit etc if you wish to stay longer. Else, stay in Batam.

Malaysia on the otherhand, the fees are about RM2000 +RM3000 for the security bond. You can bring your kids too, but all in all, getting a professional visa is far cheaper, when starting a company cost about RM4000++, and the visa fees are similar but you get to stay for 3 to 5 years.

I will go into depth in another article

Chao!

ازرين بن عبدالعزيز ماجد

The truth about credit reports and credit score.

2023-08-06T21:13:00.000-07:00

I know, being having bad credit sucks, especially if you are a foreign student or just landed in the country. Like me, it's worse off, since I have not had credit for the past 19 years , and because I am overseas, banks do not like me. Even Santander (ABBEY National) when I have been with them for over 20+ years.

No credit, or thin credit is worse off them having poor credit. So what do I have to do to get things started:

PROOF OF IDENTITY- Your drivers License. A Learners License (Provisional) would at least get you somewhere as proof. You will need the D1 form and ID + £34. They will need your passport # or Visa Share code. I don't trust them enough...so a trip up to Morriston Swansea is best.
Basic Accounts etc still require ID, and it's a chicken & egg situation, thus why your first stop is to get your ID sorted.
Voters Roll - It's to show you are legal there. And yes, a NATIONAL INSURANCE NUMBER (NINO) is needed, so you are able to work, and link your license too to it. Don't have it? Go get it yourself. Previously, we get this from the DFEE or Jobcenter, and now it's done online.

Some organisations do let you have a mere basic account, but if you are from abroad, best if you have a HSBC Account and from there, the International Team can help you get an account BEFORE YOU GET HERE. Once you are here, change yr address and it take them 3 months to get your records.

Do note some stuff with HSBC: CALL THEM AND MAKE SURE YOU ARE TAX LIABLE FOR UK ONLY and NOT DUAL TAXATION. By default, those clowns in Bangalore will say you are tax resident in XXXX and UK, otherwise you won't get any benefits from the account. And you gotta press on with them that you are a Premier customer so that you get the benefits.

For now... I will talk about those credit builder site. Yes. I paid LoqBox to up my ratings and kinda work, cos now my credit utilisation is far better.

In every aspect, it takes alot of patience and time.

Notice of filing for Trademark

2023-07-12T18:55:00.004-07:00

Legal Notice.

We at Azrin.Info, Property owner for Intellectual Mark ( ZYRAZ'S ) and it's variants (ZYRAZ) of Name and the Phonetic Sounding alike , hereby transfer the asset of Malaysia Mark No 07015090 of class 30 (manufacturing) CaseID 2007-07187 to Zyraz Technology and it's subsidiaries, including ZYRAZ Ltd (UK), ZAIRA FOODS Limited *(UK) under UK Property Mark number UK000039333093.

This serves as a legal notice of any parties using our Trade Name ZYRAZ and it's Variants within the laws of UK, Singapore, Malaysia and European Union.

Where have I been...

2023-07-11T01:38:00.001-07:00

I just realised my last post was actually 1 year ago and I was actually quite busy ..that I forgot about things.

Things Done:
Setup new Entities in UK *(Zyraz Ltd, Zaira Foods Limited) and a few bit and bobs here and that to prepare myself for mini retirement. Looking into things, the UK Market is pretty much screwed when I was there...and blimey... it's really costing a fortune there.

However, it propose a very interesting proposition for me.. getting back into the property ladder. With places like in Newcastle cheaper than my car here in Singapore... heck anything is cheaper than the COE!

Anyway, I will do some back posts....or when I am free...

For now..it's business as Usual.

Subsidy issues - Simple solutions for all.

2022-11-29T22:20:00.001-08:00

Malaysia now is in a new era of government and thus the government must listen to the Rakyat.

Fuel subsidy is a real pain in the backside for a lot of people, and they should learn from from both Indonesia and Singapore on how to run a fuel management system.

My take is as such:

Corporates:
Link with a Fleet Management Operator, such as Petronas, Shell, Petron, BHP or even a 3rd Party Fuel Reseller such as how in Singapore akin to BestFuels etc for DIESEL MANAGEMENT.

How it work? Every card / RFID Token has preset quota per day / week and month and when it's finished, it's finished and they have to pay FULL TARRIFFS. So as a company, they could pool the subsidy up to their limits, based on their usage.

Example: ABC Bus has 20 Express Buses and each uses 260L and drives 2000km....which is a lot. So the quota will be 250Lx28days=7000L per vehicle and the preset fuel will be billed accordingly, on month-to-month basis. If the company has poor payment record, then the fuel subsidy will go like the Commercial Vehicle Owners, such as Grab etc.

Commercial SME users can use APP BASED discount where they are given discount coupons for their purchases and this is subjective to their 'QUOTA' usage. Of course, like here in SG, we have 'LOYALTY' cards which is nothing new, but these cards can come with preset discounts like 10-20% depending on membership *(standard, commercial, grab-driver)++

So fuel prices MUST FLOAT. To ensure dealers are not short changed, the price they pay the company is based on the amount of fuel dispensed, less all the coupons and discount codes. Again, this is to ensure fairness and equality, as some stations lose fuel to ullage is approximately 400L of Petrol a day. As a former station owner, our net dealership discount was only 3.5sen per liter and over a tankful of 52,000L delivered, we must sell that all in a day at least before we lose money due to weather.

We actually make a little bit more money on the shop stuff, like prepaid and e-wallet topup (50c per transaction) and what others.

So, if foreigners want to refuel, they pay as per float and if B40 wants to get super cheap fuel, the subsidy may be higher because they have more coupon or subsidy whereas those with high capacity cars, get lesser coupons.

So if you run out of coupons or if you are not entitled to subsidies, then you pay the full price. Simple as that. You cannot have a BMW driver enjoying same subsidy as those with earning less than RM4000.

The mechanism could be as follows:

Motorbikes, under 150cc                  RM100 or 50L of subsidised fuel at RM1; 50L @RM1.50.
Cars under 1500cc or over 10 years RM300 or 100L at RM1 ; 100L at RM1.50
Cars 1500-2000cc                              RM200 or 200L at RM1.50
Small Trucks BDM<5T                      RM500 or 500L at RM1 (Same as fisherman)
Large Trucks / Bus                              RM2000 or 2000L at RM1

This will be far lower than the RM2.05 fuel price but they won't bleed too much as logistic cost won't go up. The bare subsidy can be applied directly to the fuel provider as a pool or likewise.

Put to this perspective, a Thai Pickup, refuel 60L x 3 a day = 180L of subsidy lost daily x 20 days...which is about 3800L of fuel subsidies or approx RM2000!
My Singaporean Brethren are no better with their fuel monkey tricks, despite the RM4 full price of RON97...so at least there is a sense of targeted subsidy for the needy.

In a way, 50L of petrol = 700km for a biker, or 20 tankfuls of a Honda Cub, 1000KM for a small car based at 10km/L consumption, and these will spur the need to budget their commute to public transport.

As for Stage Bus, maybe the LTA-PPP Bus may work well, where the bus, fuel are by the MOT, and operators charge for the Labor and Operations. At least we will know the bus will be on time and maintained.

RFID - Touch n Go ... (Join JB Rite?)

2022-05-24T16:36:00.002-07:00

Honestly I play cheat alot cos I HATE QUEUE, esp paying for toll.

What U need:

Touch n Go e-Wallet account. Top it up RM100 (can auto reload also). Recommend use a MALAYSIAN NUMBER, Maxis Hotlink is good, 30day validity, you can get free data, just topup RM5 to make it alive every 15-30 days...or Digi/Altel with their 365 plan.

Physical TNG Card..which U need to pay the stupid RM20 RoadCharge anyway. FYI, many places NO STOCK! New RFID type wait long long.

TNG RFID sticker..this one U can buy on Shoppee or many shops can do for you.

I don't like to permanent stick, so I recycled an old McD VIP decal, put in middle of it, place 6 INCHES (2 fists) from top n bottom, macam how we stick road tax disc last time, AWAY FROM ANY OTHER devices especially if U got those gantry cards. If you do, place yr gantry cards in bottom of your car...this RFID is a very luan kuku nathen type. I honestly way, have a clean left side, otherwise the alternative is more worse (paste at your number plate which must be PLASTIC)

Register, enter your plate number, and put the details, they will ask if malaysian car...just say yes.. cos Sabah Sarawak now plate same as ours...and wait for approval and activation.

Then, moment of truth, test at the RFID Lane on the toll.

If OK, then all good. Money deduct from eWallet. U will get a notification for that.

Many place , parking is all DIGITAL, so pay using your TNG App, and shop locally using the ewallet like 7-11, Lotus (TESCO) , MyDin *(CHEAP!) and more

Good luck

Selamat Hari Raya 2022

2022-05-04T20:05:00.003-07:00

Salam Semua,... Selamat hari raya... mintak maaf lah eh..tapi posting posting yang memarahi org sg isi minyak (walaupon Ron97, VPOWER) dan apa2 lain...really now getting on my nerves....

Enuff talk lah.. SHUT THE FRIGGING BORDER DOWN! We don't need all the critics and noisy motorbikers. I hate those noisy and reckless bikers! Jangan kasi orang Singapore masuk langsung ok?!

Give you business also KPKB .... while at it... we stop all ships going to PTP and Port Klang too k...

Dulu yang mulut longkang selalu kata...MENYEMAK JER ORANG SINGAPORE NI.. tak sedar sedar lagi ker? 2 tahun tutup..yang mampos rakyat Malaysia yang kerja di Singapura..kerana gaji di KL RENDAH SEKALI..tak setimpal kerja.. Doctor di HKL gaji RM5000 banding houseman S$4000..cleaner pon dapat $3000! JB terus jadi macam changlun / kelantan...kedai tutup abis.... Nak lagi ker??

INGAT SIKIT.. Kerajaan dah tak cukup duit..nak sumbat dalam ATM pon BNM tak cukup... Barangan kita mahal giler kerana supply chain kita bottle necked kat Port Klang etc compared to SG... tak sampai 6 jam... kapal dah unload+loaded up+refuelled. Kita? 3 hari masih sangkut.

Kita kalau border tutup, orang SG boleh belanja di tempat lain....Australia ke.. Bangkok ke.. London ke....flight banyak.. dialu alukan lagi pelancong yang berbelanja di tempat mereka.

Kita yang merantau di pulau penjara nie mencari sesuap nasi..kerana susah payah di tanah sendiri.... Ramai, setakat SPM je boleh digajikan lebih dari seorang doktor di Malaysia.... untuk kesenangan anak dan keluarga di Malaysia.

Don't make our lives more miserable. Kitorang pon terpaksa pakai kenderaan SG pasal nak bayar VEP etc MAHAL GILER!

Really frust lah.

Singapore - JB Border is NOW OPEN!

2022-03-30T00:14:00.001-07:00

Great News! Singapore - JB Border is now opening on 1st April 2022.

What you need:

1) LOG CARD

2) INSURANCE CERTIFICATE

3) COVID CERTIFICATE FWD Insurance

4) MySejahtera App

5) Touch N Go E-Wallet

6) Touch N Go Card

DRIVERS NOTE:

Understandably, your TNG Card is DORMANT, so you need a new one. You got 7 days to get a new one but don't kanchong, Link your TNG Card to your e-Wallet and money will be deducted from there. Then once you reach JB, GO GET A NEW ONE AND TOPUP. This is for your ROADCHARGE! All Tolls now use E-Wallet money, and your Singapore based Debit Cards can be used in the wallet.

Check your card status by clicking TOLL button on the app and if your card is INACTIVE- means you are screwed, New Card is needed. Otherwise, you may be able to get a refund if you registered it on the TNG Portal.

YOU STILL NEED VALUE IN YOUR CARD FOR ROAD CHARGE!

MYSEJAHTERA

That is similar to Trace Together in Malaysia. You can either use your IC Number as ID or Passport Number but I suggest you use your Singapore NRIC for that as your IC don't change.

One trick is for your kids to be placed in your account, so one check in, all checked in.

Vaccination Process update was covered before.

It takes 5 days to update! Bring your ORIGINAL Digital Cert!

As Malaysia tend to cock up, please keep paper printouts so you cover yourself.

Do not forget , their ATMS close at 11pm-6am!

Data Privacy and Protection.

2022-02-07T23:35:00.002-08:00

This Serves As A Legal Notice

Azrin Info Web and it's affiliated sites are hosted across the world, with Domain Services in United States and Entity Services Registered in Singapore, United Kingdom and many other locations.

While browsing the content here and throughout, your activities will be tracked by so many 3rd party services, for financial gain such as advertising or demographics or anything else for that matter and may be owned by Foreign Government Agencies.

The Data and Information may be passed and resold to other parties and by coming here, you consent that you have no right over your own data and stuff like that. In other words, ANYTHING GOES, similar to the dark web and the operators and content providers, it's agents and affiliates are not responsible for your safety and security.

Malaysia Government Regulations require us to inform you that our servers are located across multiple countries, commonly known as CLOUD DISTRIBUTED NETWORK and Services in Malaysia are operated by Zyraz Technology Berhad, Co Reg 809970H and it's subsidiary, ZYRAZ VENTURES which are all owned by Zyraz Technology Singapore, UEN SG53231076D.

Our servers are located primarily in Singapore, Malaysia, UK, US, Ireland, Germany and China via AliYun Cloud Servers. So these governments can get hold of your data without us even knowing.

As such, you are responsible over your own actions.

MySejahtera for Singaporeans

2022-02-07T23:25:00.001-08:00

TIME TO TRAVEL!!

So you are getting ready rite? Don't get caught out with our good friends in Malaysia.

As we all know, they will Roti Canai / Prata / Tosei all of us as much as possible. So here is the thing you need to do.

Your Vaccination Cert - Print off from Notarise and SCREENSHOT your cert in JPG from TraceTogether.
SingPass is needed. If you have those manual one from the center, also can but LAMINATE IT!

MySejahtera App. I will recommend get a local number + use your IC Number instead, not your passport. You can register using an email address, but you know lah how efficient their are.

As of now, I used my phone number in Singapore and DON'T WORK. Best go buy Maxis Hotlink as they have 120 day expiry and RM5 topup will let u renew (or even SOS topup)

The politicians say they will recognise our cert but we all know how it goes, and their fines are super high (RM1000 ~ S$350) so lets not be stupid.

Dogelon Mars [ ELON ] Coin - What's my take?

2021-11-02T20:19:00.003-07:00

I am a crypto fanatic, but sometimes I take the cautious step onwards. I hold some for long and some for short and mainly, it's money I can afford to throw, like going to a amusement center and indulge in my games of skills and all.

Elon Coin aka Dogelon Mars is a new coin, hyped out from DOGE and SHIBA with a current market cap of about USD900M with 1 QUADRILLION (1000 Trillion) coins issued out.

As of now, only Crypto.com is the only place selling and trading them, with a vague price of about 1 Euro to 1 Million Elon. There has no proper confirmation that ELON MUSK endorses the coin unlike what he tweeted about SHIBA the other day. I believe this is a token fee for listing in the exchange, tradable against EURO/USD/GBP/XSGD and USDT (teethers).

It’s more or less, speculative market, with some incepting at about $0.35/Mil and mostly between $1~$3 per Million Coins

Most players are Buy,Sell,Profit Take,Rebuy,Resell Cycle and with my own mid-time inception test *(where there is currently a 35M Trade Limit) , made about 150% profit on a mid range and rebuying to keep to the same level of purchases.

Only way to ride this out is to ensure the big market holding, ie with the huge number of people holding the coin so it’s more tradable.

However, I did some digging at their original contract where there are are some nasty comments with the transactions still pending by heck of alot and by over weeks and months. The developers have been keeping quiet and I honesly feel if you can afford to lose the money, go ahead..but other than that... do your proper due diligence.

Thats my 10 min wrap

#Doge #ELON #elonmusk #ELONCOIN #DOGELON

Malaysia - Home of red tapes and corrupt officials

2021-01-20T00:32:00.003-08:00

Yeah, you read it right. I am telling you blindly that Malaysian Officials Are Corrupt. Look at Najib. Convicted of CBT etc and still out walking like nothing wrong. And that things are so slow on their actions that the Malaccan Bullock Cart is faster than them.

Take example Trucking and Logistics. They are so engrossed in paperwork that the basic stuff like ROAD SAFETY and ROAD WORTHINESS is not to the par. You need a CVLB permit to be a carrier, even delivering your own stuff aka Pembawa A or Pembawa C (A or C Carrier) being A is nationwide and C is limited to State Wide and neighbouring states. This was created by the British for Indian States for monopoly. Then you need a Commercial Drivers Permit from CVLB and JPJ ( DVLA/ROV/LTA) which is a separate license and Police Permits and Roadworthy checks by Puspakom.

Puspakom is a joke and a monopoly. You wait for 6 hours to get your truck inspected. Whats so hard to check a Truck? Corrosion, Tyres, Brakes, Lights and Exhaust. All because they want coffee money. I did a commercial vehicle test in Singapore and it takes 20mins...from queue to pay to getting the cert. And this is PERIODICAL CHECKS.

Why not make it OPEN and SIMPLE? The need to control number of commercial cargo companies are just plain bull to me and it should be an Open Market thing.

Just in Singapore, you pay to buy your vehicle and make sure you get it insured and taxed properly. Older vehicles have a max span of 20 years and thats it in safety. Here we have Post WW2 trucks still running about and I mean those old TATA and Mercedes trucks without doors.

1) Simple Importation of NEW VEHICLES. No need of the AP Approved Permit stuff. Just pay 400% tax and ensure it's VTAS certified *(TUV or similar) and make darn sure it's EURO5 compliant to save the earth.

2) Single One Driving License. The need for Goods License Permits etc is just pure nonsense and must be done away with. Collect the Tax and ensure the Driver is Certified. Like in Singapore, you need a Class 4 license similar to a Cat C in UK and a +E for articulated or Class 5 for Singapore. Bus Drivers need either Class 4 or 4A + a Vocational License or in UK , just a Cat D will do. ANNUAL MEDICAL CHECKS is a must for over 50s and 3 year once for those under that. A min age of 21 will make more young blood into the Logistic and Transport industry as it pays quite well for a lower educated person.

3) Stiffer Penalties for violations such as OVERLOADING and SMOKEY TAILPIPES. In Singapore it's $1000 per HSU over 30HSU and a smokey tailpipe sure land u a $20,000 donation to the coffers. Whereas the big trucks may look overloaded , the smaller LGV are the common culprits such as those carrying food and vegetables to markets, palm oil carrying pickups etc. Honestly, I do it too and was nicked once for GVW of 5900kg on a Tata pickup truck. And it's legal MLW is 3000kg.

Why so many red tapes is that so much can be made through corrupt practices. One thing, the Puspakom is a scam when there are just 70 inspection centers against a vehicular population of over 10 million being that there are no scrappage programs. Compared to 9 in Singapore for a small population of less than 300,000 vehicles, there would need to have at least 300 centers or 3 per district where it also functions as an accident damage assessment center.

Come on.. Please ..make things streamline and do it just ONESTOP, no more CVLB hoops and JPJ hoops. Just Online.

The bitter pill for Britain

2021-01-17T19:54:00.001-08:00

As much as I am safe and sound in Singapore, Britain was home to me for quite a significant part of my life and its hurts me when I see them dragged to the mud and into the bogs.

These suggestions are the ONLY BITTER PILL I feel that Britain needs to run the course of should they want to be GREAT all over again, every since the BREXIT and it also means you lot must comply and give up a little bit of privacy and legacy for the generations to come.

IMMIGRATION - MASS AMNESTY AND REGISTRATION. - We do not exactly have a good run of how many are there in UK. Every untested and unchecked person is a health risk to the community and way to go is a MASS AMNESTY. Of course, you pay some sort of money , say 500 quid per person to be REGISTERED as a LEGAL RESIDENT of the United Kingdom. It will sure resolve tons of labour shortages as these people will want to work. Call it an Intermediate Green Card. After 5 years of working and contributing, you get to be Naturalised.

MASS REGISTRATION is needed for EVERYONE. Biometrics, DNA etc and Single ID Number not just linked to your DVLA Number or National Insurance Number. Sometimes, it does not tally and link each other. One Number and Photo ID. One Agency for all the Numbering - Department for Immigration and Nationality Records. Numbers could follow many countries ID system such as BIRTHDAY-XX-YYYY like in Malaysia where the XX is where the state/region they were born etc or BIRTHDAY-ABCD for the CHECKSUM such as 20203101ABCD as there are about 1000+ born in UK Daily.

Talking about privacy is that this is to PROTECT YOUR PRIVACY and do not discriminate your gender just showing your birthday. People who scream high and low of privacy are those who wish not to pay taxes and go on the take, and getting paid in cash. One Single Number will simplify the HMCE, JobCenter, DWP, NHS etc so your records are NOT MESSED UP.

Reason being, if Reshi wants to be like Singapore, some things NEED TO CHANGE. One Identity System is needed. From Company formations, to property tax and licensing and to swift dispersement of services like MEDICAL.

Example. You are drunk on a Saturday night and you pass out. Ambulance picks you up and with your picture, they can already determine if you have allergies, High BP or some medications which , if wrongly administered, can be fatal. I am a Doctor and I almost make this stupid mistakes when I thought is a drunk but is a Diabetic Epileptic Shock.

You go to the Post Office to collect your Dole, and all you need is your ID number or better , just a Facial Scan. It's already available in Facebook for crying out loud that it's quite simple. Tap your Pin and swipe your finger, and it authenticates your ID. Presto, Money comes up!

CHANGE THE DWP SYSTEM. How? The more you work, the more you save up and the more you can have in your old age or when you need it. Singapore's CPF and Malaysian KWSP EPF systems work. By using your own Provident Savings, your load on the NHS reduces too. And if you talk about the bill being super expensive, the Treasury will subsidise 80% of the medical for Hospitalisation and Costs and you pay about a tenner for a trip to the GP. Foreigners and "Extras" on the services like being in the 4 bedder ward or Singletons will cost you more as the subsidies will be far lesser.

Definitely will make you able to come to any GP that you like, and get treated for a tenner and without a need of a lengthy appointments. Even when you are skint, you can come to a PolyClinic aka Primary Care Center to be treated for a very token fee, or they can send you a bill later.

Singapore's Health Service is partly commercial and owned by the government. If you have insurance, then you can pop to the more lush hospitals if you wish to.

MANDATORY 30 DAY QUARANTINE. That looks easy, but EVERYONE STAYS HOME! If you do not have money for fuel etc, then there is a one off FUEL TOPUP for the 30days worth, say 100 quid and Food Rations to be delivered and One Off Dole Disbursement for all. That means MANDATORY ID again to the point of #1

HIRING OF UNEMPLOYED FOR MASS CONTACT TRACING & TESTING. There are a few million unemployed and I believe the government should just pay them 3000 quid a month, without any tax or deductions plus they are still on the dole for their Volunteer Services. This is a RISK ALLOWANCE. You will need about 400,000 people and draft up the yobbos to do the job, for marshal etc. We do not need to use the ARMY or NHS RESOURCES FOR THIS! Give em a medal after 6 months of service to make them proud of serving the country.

MASS TESTING. Enough being said above and it's time to get things done. And NOT JUST ONE TIME. 2 x Testing. And after all these done which will be before spring, we can isolate and get things on the way.

MASS VACCINATION is something we do not need to say. Once these people who are cleared, wearing a GREEN TAG will go for inoculation.

ULTRA HIGH SPEED FIBER AT EVERY HOME & BUILDING. That is key to Singapore success and to hell with Councils and Health and Safety nonsense. Get this done so everyone is on the digital landscape where every home can be a business.

Even in New Zealand, fiber has reached even to the farmers barn and it's a shame with the current state of internet connectivity in UK and where the remote parts of Scotland do not even have proper utilities such as water and electric mains.

BUILD MORE HOMES. We need to make homes super affordable. There are many parks and industrial spaces which could be reallocated to high rise units and when I say high rise, is 40 storey high which in a football field, will yield about 10,000 units. Think of kicking out Tesco or Sainsbury in Whitechapel and replacing with that and still you have your Sainsbury and local there. And connected to the overland train station and Tube.

These units will even give better safety and lessen the parking woes with even bigger green spaces and community use-enabled spaces such as kindergarten, parks, running tracks etc. Imagine running on the high deck in London, 50 floors up. The Pinnacle Duxton is an example.

These can be completed as early as 3 years using the Pre Fab method. Jobs will be created. And no more GRENFELL as it will be quipped with Fire Hydrants, Sprinkler System, Stop Smoke Lobbies etc.

By reassigning land and repurchasing the old land for high rise, London can be the New Asia of Europe without the fuss of EU telling us what to do.

And for the food shortage, then SELL LOCAL. Buy from Asia.

Revolut Metal - is it for me?

2020-12-02T22:21:00.002-08:00

First off, the Metal plan was initially BRILIANT until the MAS laws step in. Paying $240/yr for 1% of cashbacks makes no sense when the concierge is gone and free lounge access is up in the sky, ie NOT FLYING~

Being a business person, I would appreciate having both Visa and Mastercard as an extra perk for the metal holder plus that the limit of having 2 physical cards are a bummer!

Getting customer service is somewhat NON EXISTENT ANYMORE and calling the 800 number won't get you anywhere. They should at least invest in some sort of offline support for PAID MEMBERS who may need emergency services etc, esp when then the app goes south. Emailing them DO NOT WORK!

I AM NOT PAID FOR THIS REVIEW but other than the card looks nice and no withdrawal in Singapore, it devalues the card totally and you have other better free cashback and goodie cards like MCO or sort.

So my rating is now 3.5* or 74% satisfaction.

And if you want one, a normal one for now, you can sign up using my direct link and leave a comment here too.

Of Blockchain, Crypto and Coins – The Dinosaur’s Edition

2020-11-18T22:40:00.008-08:00

Many do not understand what is these hype all about, of Bitcoin, Ledger and stuff like that and even worse, in the 21^st century, even financial institutions are adopting the Blockchain technologies that come with it. So here is the brief run down for everyone to be up to speed.

Cryptography is the method of ENCRYPTING or shuffling of data into a scrambled piece of information that only the sender and receiver can understand. Something like the Nazi Germany’s Enigma machines during WW2. It contains a cipher text and a Private Key which is needed to unlock or decode the message. Crypto Coins are tokens or coins in a casino minted off a factory called a Mine which the limit is defined in the initial definitions or Contract, something like the Constitution. There is a limit to how many can be mined and / or circulated.

Coins or Tokens are what these assets are called and in the defined currency. Usually we will hear BITCOIN , ETHEREUM or tokens and the currency code will be like BTC , ETH or others. They are minted by means of a REWARD SYSTEM or MINING where each block they mined, they get a certain number of coins + the mining fees.

What is a Miner and Mining Fees. To pay for a transaction or send a message, you need to pay some sort of fee, like electricity tariffs to the producer. The Miners will mine the block, encode and sign the ledger collectively and share the rewards and finders fee. Think of it as the Postman who collects your checks for the bank to process and gets a fee on the postage stuck to the envelopes. Whoever pays higher fees gets their transactions through first and the cheapest will take the last priority, mainly there is a limit to the size of the queue.

A Block is a group of transactions , bound together as a collective and at the end of the specified block, defined as either by size of the block or time is signed at the end of the block and encoded to the chain. Think of your class homework is checked by the teacher , signed off and results *(Hash of the Transactions) is recorded into the Report Book and the report book is signed by the headmaster. So before next year starts, your headmaster checks the report book signed by the teacher, add that to the start of the next year report and it goes on and on. That is called a Chain.

Because it’s been signed off by the Headmaster, the results cannot be changed on your homework or test results because everyone can have a copy of the report books. That is called the Ledger. The ledger sits on the block chain.

There are some coins that piggy back on some other infrastructure. It may be sitting on the XCP CounterParty network which rides on Bitcoin BTC network or Ethereum based ERC-20 for Tokens or ERC-721 for non-fungible tokens *(more like a fantasy coin or other uses). The coin or token require a smart contract to create and issue the coins and some due diligence is needed. A transaction fee of satoshis *(about USD7 worth of BTC at current time) or Ether Gas *(called wei ~ approx. 25c in fees) are needed to encode the message into the chain.

DAX or commonly known as Digital Asset eXchange is an exchanger of assets. Think of it as a stock market where you trade your money for future contracts like Oil etc or Forex like USD-GBP-JPY etc. A DAX sometimes sell you assets in your local currency and at times allow you to trade in different pairs such as USDT-BTC-ETH or other major pairs.

Note that USDT is a TEETHER where the asset is pegged against USD safely kept in a custodian.

Do check and do a proper full due diligence. Think of them as Promissory Notes by the Central Banks. There are stable coins for currencies like the Singapore Dollar xSGD which are acceptable by exchangers such as Cryto.com, CoinHako, Luno and others.

Easy ScotCoin Migrate

2020-10-06T18:56:00.000-07:00

DEADLINE FOR MIGRATION IS 31 OCT!

The Scotcoin Project have posted many videos on how to Migrate from Scotcoin V2 to Scotcoin V3

This is the SIMPLE GUIDE!

These are needed:

$10 worth of BTC in your V2 account
Your ETH ERC Address.
Google Account (for your DRIVE)

Step 1) CREATE YOUR OWN ETH Wallet using GETH

Download GETH from the OFFICIAL ETHEREUM WEBSITE. Its version 1.9.22
Install GETH
run geth 1st time ...*(wait for a few minutes to cache up)
create a new wallet with the command geth account new

It will ask you for a password
It will show you your address
KEEP YOUR PRIVATE KEY SAFE! In this case the address is 0xCbe48547de20245b3ca39D105C953cA582386754
Verify the address keystore.
the keystore will always show address and the cipher. IF YOU LOSE YOUR PASSWORD, YOU ARE SCREWED!!!
Windows users: your keystore is in c:\program files\geth\keystore or %appdata% ...so look carefully where it chuck it.
Copy this file to your Google Drive as safe keep

Step 2) Login to your Counterparty Wallet

Make sure you have about $10 of BTC to burn
Send all your Tokens to the Burn Address *(TEMPLE WILL SEND YOU AN EMAIL Burn Address)
https://scotcoinproject.com/migration/ is the Migration homepage.
Send all your SCOTCOIN to the BURN ADDRESS
Attach the screenshot of the transaction and send to temple.
Check your BALANCE on the Public Space. In my case it will be https://etherscan.io/address/0x162c28e126a97d0009158fbda0ca93e6e9cc13d0#tokentxns where my address starts with 0x162

Thats it! You now have Scotcoins migrated.

Now..according to the Scotcoibn V2 Blockchain..there is 2600 token holders that did not migrate off, so please migrate it back!

That is alot of tokens running about.

Did I forget to say.. if you have any left BTC in your Counterparty wallet, you can either donate to me or U can open your own Crypto.com wallet and use it to spend in the shops vis of a Debit card.

It's quite simple anyway.

What is the Living Will?

2020-09-24T20:14:00.002-07:00

Sometimes, I get that question.. and it's a pain in the backside for me too, since I am a Singaporean and my darling wife is from Malaysia and since we got kids and Singapore don't bother much, it's a real pain to put wills into perspective.

So What I am going to do is radical since the new age, VIDEO DEFEATS THE PAPER. And that the fact that I do not trust lawyers and their fees et al. And whether they are in my best interests.

So here is my Will.

I, AZRIN BIN ABDUL MAJID, commonly known as Rapheal Azrin who is born in Singapore hereby declare the following:

1)That my legal and legitimate Children are as follows:
ATHIRA AZRIN
SARAH ATIQAH AZRIN
DANIAL IRFAN
MUHAMMAD DANISH
NUR RAUDHAH AZRIN and at this time and date, are FIVE (05) Only.

2)That my legal Wife is _________________________(I will not name here).
3) That my Trustee in the power of attorney and discharges is NURASYIKIN ABDUL MAJID, whom is my Blood SISTER (RELATIONSHIP) {can't provide my recipient as trustee}
4) That I have insurance Policies in Singapore, UK, Malaysia (State Countries) with pension policies.
5) That I have willed to CPF Board of my will , Dated ____________ to be correct.
6) That my Religion is ISLAM and I am creating this in accordance to the laws and rites of religion; and of that, upon my death, my remains be treated as such in accordance to my beliefs.
7) That my CPF related health and insurance policies are willed accordingly to my suiting.
8) In the event of my demise (BRAIN DEAD/MEDICALLY INCAPACITATED), my Trustee will be given the authority to donate my organs whilst I am still on artificial support or terminate my life. And in this instance , I am ordering a DO NOT RESUSCITATE. However, it is up to the medical professional to ascertain my life viability and Quality of Life.

9) That my Digital Assets is kept in an offsite location known to the Trustee and to execute to the best of interests.

Signed/Dated on timestamp

Singapore 2020

Investing in Data Disaster & Recovery Post COVID19

2020-09-24T20:11:00.002-07:00

The most dreaded word.. DATA DISASTER & RECOVERY bring shivels into many IT professionals and managers alike. It's like WHAT IF WE LOOSE ALL OUR DATA senario. And recently, me being a real jack nerd... had it's own share of kaplui when my kid used my file server to host his ROBLOX games. Ouch.

Here are some IT situations than can resolve this. Onsite , Cloud, Hosted or Crowd.

Onsite means - use a hardware to back yr data up. Server or likewise. Offsite means it will be hosted at a Webhost / Co-location or otherwise and it's almost the same as an OnSite and Cloud... you know what its all about.

Crowd is a new kind of technology but with an older mind attached to it. Think of it as the old Napster / MIRC piracy days where one copy of the file is copied across so many peers and if you need just one file, so many peers can send you the file across the globe, bits and bobs of it.

With the era of BlockChain , ERC 2.0 and et al, I have been toying about with Tardigrade.io using Storj as the underlining backbone of the storage hosts aka Nodes. Each copy of your file is Fragmented and each fragment is hosted by at least 5 nodes and encrypted.

It looks to be an S-3 compatible BLOB technology comparative to the AWS or Azure and it's not blocked by the Chinese Firewall. And in terms of process and speed, that is up to the network satellite and cross connectivity as some are linked with E1 and some like me are linked up with GBE connections and a more preferred node.

Price wise at 1c/Gb for the store will work to about 1.5c per GB of data delivered meaning a typical LTO of 6TB will likely cost you about $100 without the need of the LTO Device, Tapes and Power.

Compare this against a $20k CAPEX cost for the drive alone and tapes.. ouch... it's a hands down winner.

Being having users all over the place, then we have the luxury of the data being segregated and not having a bottle neck across the data feed.

"But I die die want to have onsite" is a mentality which is GOOD that works well with the CROWD.

I have a customer which is a Hogger... 450TB of data... ONSITE...and if anything goes... I say God Save Em... and with a 1Gbps link (Read GPON-upload is 500Mbps)... means it will take em 79 days to upload the whole frigging data onto the network, which is a killer.

That being said, a good management of data is critical to this business case.

Opening up Johor for Business.

2020-09-07T22:11:00.003-07:00

Headline:

From malls to hotels, the Singaporean haunts that have gone quiet in Johor Bahru amid COVID-19

compliments of CNA today, and this is what I feel I need to say.

This is why you should not talk big and blame your small brother for all your woes. SG-JB is a Symbiosis of coexistence and needed each other to support and grow, otherwise it will be the same like Rantau Panjang in Kelantan or Bukit Kayu Hitam in Kedah where all drive to Danok for stuff.

One way is to Bite the frigging bullet and barricade Johor off from rest of Malaysia and check EVERYONE in JOHOR. Costly, but that will be the small step towards cleaning up the illegal immigrants and other criminal and viral activities.

Then we slowly get things done and grow the economy. Poor Malaysian friends who need us and we too, poor us who are suffering cos of the high fuel and food costs.

Otherwise, Bintan could do the same and we all flock there.

You see, Singapore NEEDS the workforce to get things running, such as construction and services and we need an outlet to spend the money and get that itch out. We need to relax too. Being cooped up at home, SLS HLE WFH all are draining our mental might and we might be breaking into shatters when this is over.

Forget the airport, we can forgo that, we need our massages, hot spa and other stuff...and FOOD .. DELICIOUS FOOD. Don't forget one full shopping trolley in SG could cost about $300 compared to RM300 in Malaysia with like for like.

Yes, Malaysians will make noise that they cannot get into Johor without passport and checks but if every state is cleared, of pests, illegals and viruses, then we all can enjoy. 400,000 Malaysians can come to work and thats at least $500M or RM1.5 billion spent in Malaysian economy monthly.

What you feel?